Search Results (4,384)

Guidelines provide specific recommendations based on the best available medical knowledge, summarizing and balancing the advantages and disadvantages of various diagnostic and treatment options. Currently, consensus methods are the best and most common practices in creating clinical guidelines, even though these approaches have several limitations. However, the rapid pace of biomedical innovation and the growing availability of real-world data (RWD) from clinical registries (containing data like clinical outcomes, treatment variables, imaging, and laboratory results) call for a complementary paradigm in which recommendations are continuously stress-tested against high-quality, interoperable data and auditable artificial intelligence (AI) pipelines. AI, based on information retrieved from patient registries, can optimize the process of creating guidelines. In fact, AI can analyze large volumes of data, ensuring essential tasks such as correct feature identification, prediction, classification, and pattern recognition of all information. In this work, we propose a four-phase lifecycle, comprising data curation, causal analysis and estimation, objective validation, and real-time updates, complemented by governance and machine learning operations (MLOps). A comparative analysis with consensus-only methods, a pilot protocol, and a compliance checklist are provided. We believe that the use of AI will be a valuable support in drafting clinical guidelines to complement expert consensus and ensure continuous updates to standards, providing a higher level of evidence. The integration of AI with high-quality patient registries has the potential to substantially modernize guideline development, enabling continuously updated, data-driven recommendations. Full article

Objectives: This study sought to identify sociodemographic and clinical predictors associated with the absence versus presence of alterations in mental, neurological, cardiovascular, osteomuscular, and pulmonary conditions, to provide information towards targeted interventions for non-communicable diseases (NCDs) in urban Colombian populations. Methods: A cross-sectional analysis was performed on 2495 patients (70.1% women) from public health facilities in Bogotá, using the Colombia Open Data “Enfermedades Crónicas” dataset collected between January and December 2023. Associations between sociodemographic variables (sex, age groups, education, and ethnicity) and clinical variables (BMI, type of disability, COVID-19 vaccination status, psychiatric risk, and the modified Medical Research Council dyspnea scale) were examined in relation to health outcomes. Data cleaning involved the exclusion of 107 outliers identified by z-scores >|3| using Microsoft Excel 365. Categorical variables were summarized using frequencies and proportions, and Pearson’s chi-square tests were applied to assess bivariate associations (e.g., BMI–health conditions, and sex–disability associations). Multivariable Firth’s penalized logistic regression models (implemented in Python 3.14 and Jamovi 2.3) were used to predict the absence of alteration (reference category: presence), adjusting for multicollinearity (variable inflation factor, VIF) and events-per-variable ratios. Odds ratios (ORs), 95% confidence intervals (CIs), and two-tailed p-values were estimated, with statistical significance set at p < 0.05. Results: Women predominated in obesity (81% vs. 19% in men, p < 0.001) and in unaltered conditions (e.g., 71% of cases without pulmonary alterations) but exhibited a lower crude prevalence of disability (6% vs. 16% in men, p < 0.001). Men represented higher proportions of alterations (e.g., 53.8% of pulmonary cases vs. 46.2%, p = 0.006) and mental disabilities (70%, p < 0.001). Firth regression models identified the following predictors: for mental alteration, a single COVID-19 vaccine dose (OR = 2.39, 95% CI 1.12–5.09, p = 0.024), occupation (OR = 1.07, 95% CI 1.05–1.10, p < 0.001), BMI (OR = 0.96, 95% CI 0.93–0.98, p < 0.001), and disability (inverted OR = 4.35, 95% CI 2.56–7.69, p < 0.001); for neurological alteration, occupation (OR = 1.15, 95% CI 1.10–1.21, p < 0.001) and disability (inverted OR = 3.45, 95% CI 1.43–8.33, p = 0.006); for cardiovascular alteration, BMI (OR = 1.02, 95% CI 1.00–1.03, p = 0.042); for osteomuscular alteration, occupation (OR = 1.03, 95% CI 1.01–1.06, p = 0.011); and for pulmonary alteration, occupation (OR = 1.07, 95% CI 1.03–1.11, p = 0.001). The models demonstrated a moderate to excellent goodness-of-fit (R² = 0.25–0.72). Conclusions: Sex, BMI, disability status, occupation, and COVID-19 vaccination status emerged as key predictors of NCD-related alterations, highlighting specific vulnerabilities such as partial immunization for mental health risk, and disability for mental and neurological outcomes. Targeted interventions, including completion of vaccination schedules, mitigation of occupational exposure, BMI management, and disability-inclusive care, may reduce health disparities and support PAHO/WHO 2025 targets. Longitudinal studies are recommended to establish causal relationships in the context of Colombia’s fragmented subnational NCD evidence base. Full article

Digital–intelligent integration (DII) has emerged as a pivotal driver for high-quality urban development, offering a pathway to overcome pressing resource and environmental constraints. By harnessing data as a core production factor and integrating advanced intelligent technologies, DII can substantially elevate urban green economic efficiency (GEE). This study constructs a quasi-natural experiment using the staggered rollout of national big data comprehensive pilot zones (initiated in 2012) and smart-city pilot programs (from 2016 onward). Employing a rigorous staggered difference-in-differences (DID) estimator on panel data from 279 Chinese prefecture-level cities over 2010–2021, we find that DII causally increases GEE by 5.03 percentage points (p < 0.01). This benchmark result remains robust across a comprehensive set of checks, including parallel-trend validation, placebo tests, double/debiased machine learning, two-stage least squares with historical IT-sector instruments, and controls for overlapping policies (e.g., ETS, low-carbon pilots, green finance zones). Mechanism analysis, conducted via a sequential 2SLS control-function approach with lagged mediators and Sobel–Goodman mediation tests, reveals three theoretically grounded channels: (i) enhanced urban ecological resilience (mediates 62%, z = 4.68), (ii) accelerated green technological innovation (55%, z = 4.12, measured by IPC/Y02 patent share), and (iii) heightened entrepreneurial vitality (58%, z = 4.39, new firms per 10,000 residents). Heterogeneity tests show pronounced effects in growing and mature resource-based cities (+1.21% and +11.21%), high-fintech cities (+11.35%), and high-river-density areas (+10.29%) but insignificant impacts in declining resource-exhausted cities (joint F p = 0.08). This study makes four key contributions: (1) it innovatively constructs a continuous DII policy variable by exploiting the synergistic timing of dual pilots, thereby overcoming the limitation of analyzing policies in isolation; (2) it opens the “theoretical black box” by integrating institutional theory and information economics into a unified conceptual framework that explicitly links DII to GEE through reduced transaction costs and alleviated information asymmetry; (3) it enriches the mediation identification strategy in staggered settings using 2SLS control functions and sequential G-estimation, addressing endogeneity in intermediary variables more rigorously than traditional three-step approaches; and (4) it delivers nuanced evidence on the contextual conditions (when and where) under which DII yields the strongest green dividends, providing actionable guidance for China’s “dual-carbon” goals and the global green transition. Full article

A mix of internal and external factors affect farmers’ recycling practices with regarding pesticide packaging waste. However, most of the research that has been done so far has concentrated on the individual effects of these elements rather than providing a clear explanation of the intricate mechanisms by which a variety of internal and environmental factors work together to drive recycling behavior. This study builds an integrated “internal–external” factor analysis framework based on Lewin’s Behavior Model, integrating Organizational Support Theory and the Theory of Planned Behavior to close this gap. We examine the many configurational pathways influencing farmers’ willingness to recycle pesticide packaging debris using fuzzy-set Qualitative Comparative Analysis (fsQCA). The results showed that there are four different configurational approaches that lead to a high readiness to recycle: the information–norm interaction-driven path, the capability–belief-driven path, the norm–emotion endogenous-driven path, and the psychology-driven dominant path. Farmers’ attitudes toward recycling were found to be a key component in all four routes, indicating that it is essential to attaining high recycling willingness. The findings of this study offered policy insights for encouraging recycling behavior and assisted in identifying the intricate causal processes of multi-factor synergy impacting farmers’ propensity to recycle pesticide packaging trash. Full article

Background/Objectives: Esketamine-related dissociation is a transient, pharmacologically induced altered state that differs from the trait-like pathological dissociation typically observed in trauma-related conditions. While most studies have quantified these effects using the Clinician-Administered Dissociative States Scale (CADSS), patients’ subjective phenomenology and meaning-making remain underexplored. This qualitative exploratory study investigated how patients narrate, interpret, and integrate dissociative experiences occurring during intranasal esketamine treatment for treatment-resistant depression (TRD). Methods: We conducted semi-structured interviews with 36 adults with TRD who were receiving intranasal esketamine in outpatient settings in Northern Italy (2022–2024). Interviews focused on the most salient dissociative experiences during treatment. Transcripts were anonymized and analyzed using inductive thematic analysis. Two researchers coded the data independently; discrepancies were resolved by consensus, and recruitment continued until thematic saturation was reached. Results: Four experiential domains emerged: sensory alteration and perceptual flow (10/36, 27.8%), time suspension and chronological drift (21/36, 58.3%), body and space alteration (20/36, 55.6%), and psychic distance from suffering (30/36, 83.3%). While a minority described transient distress or loss of control, most narratives framed dissociation as neutral or subjectively meaningful, often associated with a temporary reduction in ruminative self-focus and depressive distress. Conclusions: A narrative, phenomenological lens complements quantitative research by clarifying what esketamine-induced dissociation feels like to patients and how it is appraised in context. The findings do not imply a causal or mediating role in antidepressant efficacy. Rather, they suggest that dissociation functions as a transitional subjective state, the clinical relevance of which depends on anticipation, framing, monitoring, and integration. These results support the use of structured psychoeducation, in-session support, and post-session integration within real-world esketamine programs. Full article

The cruise shipbuilding industry faces significant disruptions stemming from escalating trade frictions and regional conflicts which threaten its operational and economic sustainability. Enhancing supply chain resilience is thus crucial for sustainable development. This study identifies critical resilience factors and examines their interrelationships within growth-stage cruise shipbuilding supply chains. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Matrix Cross-Reference Multiplication Method (MICMAC) analysis are integrated to explore causal linkages, hierarchical structures, and driver-dependence dynamics. The analysis reveals that customized demand responsiveness, learning organization, specialized industrial clusters, and inter-industry collaboration are fundamental causal drivers. In contrast, knowledge stock, risk culture, and final-assembly orchestration serve as critical mediators. Based on these findings, we propose distinct resource-contingent strategic pathways for managers. This study provides an actionable framework for building resilience, offering critical guidance for securing the sustainable development of the cruise shipbuilding industry amid uncertainty. Full article

Background/Objectives: Out-of-hospital cardiac arrest (OHCA) outcomes remain poor and vary widely across communities with socioeconomic deprivation. This study examines whether delays in emergency medical services (EMS) activation, the earliest link in the Chain of Survival, mediate the association between regional deprivation and OHCA outcomes, and whether this effect is modified by bystander cardiopulmonary resuscitation (CPR) status. Methods: We analyzed adult patients (aged 18–80 years) with witnessed, EMS-treated OHCA of presumed cardiac etiology from the Korean nationwide OHCA registry (2015–2022). Regional deprivation was defined by the Regional Deprivation Index and dichotomized into deprived (top 20%) vs. non-deprived areas. Timely EMS activation, defined as collapse to EMS activation, was measured as an awareness time interval (ATI) < 5 min. Outcomes were good neurological recovery (CPC 1–2) and survival to discharge. Causal mediation analysis within the counterfactual framework quantified the proportion of the association mediated by timely EMS activation, with stratification by bystander CPR status. Results: Among 43,032 patients, 6.1% resided in deprived areas. Deprived areas had lower bystander CPR (22.6% vs. 36.3%) and timely EMS activation (67.8% vs. 75.6%) (p < 0.05 for all). Regional deprivation was associated with poorer outcomes (good neurological prognosis: aOR 0.46, 95% CI 0.39–0.55; survival: aOR 0.65, 95% CI 0.57–0.73). Mediation analysis showed that ATI < 5 min accounted for 3.7% of the total deprivation effect on good neurological outcome and 7.9% on survival, with stronger mediation among patients receiving bystander CPR (7.9% and 14.7%, respectively). Conclusions: Regional deprivation is significantly associated with poorer OHCA outcomes, partly mediated by delays in EMS activation, particularly among patients who received bystander CPR. Interventions to enhance early recognition, rapid EMS activation, and bystander CPR in deprived communities are critical to improving survival equity after OHCA. Full article

Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extensive research on rockburst mechanisms and mitigation, the causal analysis of individual events remains challenging due to the complex interaction between seismicity, geological conditions, stress redistribution, and operational factors. This study proposes a structured and multidisciplinary methodology for the causal analysis of rockbursts in deep high-stress tunnels. The methodology integrates seismicity analysis, geological and geotechnical characterization, operational assessment, field damage inspection, and hypothesis-driven interpretation to systematically reconstruct the sequence of processes leading to rockburst occurrence. The proposed approach is applied to a rockburst that occurred in 2020 in the Conveyor Belt tunnel (TC) of the Andes Norte Project, El Teniente Division, Codelco (Chile). The event reached a local magnitude of Mw = 1.7 and caused significant damage to tunnel support systems. Results indicate that the rockburst was associated with excavation- and blasting-induced stress redistribution, leading to the activation of a sub-horizontal rupture plane and subsequent damage propagation toward the excavated tunnel. The methodology provides a transparent and adaptable analytical framework for integrating multidisciplinary data into a coherent causal interpretation. Although demonstrated using a competent and brittle rock mass, the framework can be adapted to other deep tunneling projects under high-stress conditions by adjusting the governing parameters according to site-specific geological, geomechanical, and operational characteristics. The proposed approach supports improved understanding of rockburst mechanisms and informed decision-making for seismic risk management in deep underground excavations. Full article

Background: Congenital heart diseases are the most common congenital malformations, affecting 4 to 9 per 1000 children, with increasing global prevalence. As surgical mortality rates decline, the focus has shifted toward improving the quality of life and perioperative outcomes for pediatric patients. Multidisciplinary rehabilitation, including osteopathic care, is increasingly incorporated into recovery programs. Osteopathic manipulative treatment combines manual techniques with lifestyle guidance to alleviate postoperative pain and promote recovery. This project report describes the impact of osteopathic manipulative treatment (OMT) on pain and somatic dysfunctions in hospitalized pediatric cardiac patients, using validated pain assessment tools. It presents a retrospective analysis of data collected as part of a humanitarian volunteer project. Methods: The project report follows a retrospective descriptive study design, using patient note forms from children aged 0–18 years undergoing cardiac surgery at the Sri Sathya Sai Sanjeevani Center in India between October 2023 and March 2024. A total of 29 experienced osteopaths recorded pain assessments at three time points—pre-surgery, post-surgery, and pre-discharge—using age-appropriate pain scales (FLACC, Wong-Baker Faces, and Numerical Rating Scale). Somatic dysfunctions were evaluated and classified using ICD-10 M99 codes. Data analysis involved descriptive statistics and pre-post comparisons using statistical software (Excel and OPENEPI). Results: The study included 564 children (60.5% male, mean age 5.8 ± 4.3 years). The most common congenital defects were ventricular septal defects (38.5%) and tetralogy of Fallot (21.6%). The average hospital stay was 15.9 ± 11.1 days. Significant reductions in pain scores were observed from the Intensive Care Unit to the postoperative ward (p < 0.001). Similarly, somatic dysfunction severity decreased significantly across hospitalization phases (p < 0.001). The thoracic region and rib cage were the most frequently affected areas. No adverse events related to osteopathic manipulative treatments were reported. Conclusions: This project report indicates that osteopathic manipulative treatment is safe and feasible for pediatric patients undergoing surgery for congenital heart disease. Pain scores and somatic dysfunction severity decreased during hospitalization. However, the lack of a control group, the heterogeneity of the patient population, and the short observation period limit the ability to draw causal conclusions. These findings provide a descriptive framework for integrating OMT into multidisciplinary pediatric cardiac care. Future studies should involve prospective, multicenter designs with control groups and longer follow-up periods to assess clinical, functional, developmental, and quality-of-life outcomes. Full article

Background: Colorectal cancer (CRC), with high incidence but low rates of early diagnosis, poses significant challenges to public health worldwide. Lipid metabolic reprogramming has been closely associated with CRC occurrence and development. This study aimed to identify key fatty acid metabolism-related molecules involved in the development of CRC and to explore potential prognostic biomarkers and therapeutic targets. Methods: Based on The Cancer Genome Atlas (TCGA) data from colon adenocarcinoma (COAD) patients, we applied weighted gene co-expression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) to identify fatty acid metabolism-related signature genes in CRC. Expression validation and prognostic analysis were conducted. Summary-data-based Mendelian randomization (SMR) was used to infer causal relationships between target genes and CRC. Single-cell transcriptomics and immune infiltration analysis elucidated underlying pathogenic mechanisms. Cellular and animal experiments validated tumor-suppressive effects and lipid metabolic regulatory mechanisms. Results: RPS6KA1 and CHGA were identified as fatty acid metabolism-related signature genes in COAD. Only RPS6KA1 was significantly downregulated in COAD and negatively correlated with poor prognosis (p = 0.0069). SMR confirmed its tumor-suppressive role, potentially associated with enhanced antitumor functions of CD8⁺T cells and follicular helper T cells. In vitro and in vivo experiments demonstrated that RPS6KA1 inhibits malignant progression of colon cancer and modulates fatty acid metabolism. Conclusions: Integrated multi-dimensional bioinformatic and experimental analyses reveal that RPS6KA1 remodels fatty acid metabolism and suppresses malignant progression, indicating its value as a prognostic biomarker in CRC and providing new insights for therapeutic strategies. Full article

Background/Objectives: Acute respiratory failure (ARF) is associated with a high mortality. This study aimed to explore the association of arterial partial pressure of carbon dioxide (PaCO₂) in relation to survival outcomes in mechanically ventilated patients with ARF. Methods: This multicenter retrospective cohort study integrated the data from the eICU Collaborative Research Database (eICU-CRD; n = 10,946), the Medical Information Mart for Intensive Care IV (MIMIC-IV; n = 6683), and clinical records from two university-affiliated intensive care units in China (n = 410). The patients were categorized into low, normal, and high PaCO₂ groups using a restricted cubic spline model to explore the relationship between PaCO₂ and mortality. The 28-day survival distributions among the three groups were compared using Kaplan–Meier curves, with statistical significance assessed via the log-rank test. A multivariable Cox proportional hazards model was constructed to evaluate the independent prognostic value of PaCO₂ for multiple complications. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for the low and high PaCO₂ groups relative to the normal PaCO₂ group. Results: A U-shaped relationship was observed between PaCO₂ and mortality, with both low PaCO₂ (<36.4 mmHg) and high PaCO₂ (>57.9 mmHg) associated with an increased mortality risk. Kaplan–Meier survival analysis demonstrated that patients in the intermediate PaCO₂ range (36.4–57.9 mmHg) exhibited the highest survival rate (65.2%), whereas those in the low and high PaCO₂ groups had significantly lower survival rates (60.0% and 63.2%) (log-rank test, p < 0.001). Adjusted survival analyses further revealed that complications such as sepsis and chronic kidney disease significantly influenced the mortality across PaCO₂ strata. Compared with the intermediate PaCO₂ group, the hazard of death increased by 25.5% in the low PaCO₂ group and by 18.9% in the high PaCO₂ group. Conclusions: This retrospective analysis indicates that arterial PaCO₂ levels within the optimal range are associated with improved survival in patients with acute respiratory failure (ARF) on mechanical ventilation, but prospective studies are needed to establish causality and consider potential confounding factors. Full article

The rapid development of autonomous vehicle technologies represents a major transformation in contemporary transportation systems; however, their successful integration depends not only on technological maturity but also on societal acceptance. This study investigates public attitudes toward autonomous vehicles, with particular emphasis on generational and gender-based differences, aiming to identify key factors influencing acceptance, usage intention, and purchase-related decision-making. A quantitative, cross-sectional research design was applied using an online questionnaire survey conducted between January and September 2025. The final sample consisted of 655 respondents, with a balanced gender distribution and representation across multiple generational cohorts. Statistical analyses included one-way and two-way analysis of variance (ANOVA), complemented by non-parametric tests when distributional assumptions were not fully met. The results indicate significant generational differences across all examined dimensions. Younger generations, particularly Generations Y and Z, exhibit significantly higher willingness to try autonomous vehicles, greater openness to new technologies, and stronger consideration of autonomous functions in vehicle purchasing decisions. Gender-based differences were also identified, with men generally demonstrating higher technological openness than women. Moreover, a significant interaction effect between generation and gender was found, suggesting that gender differences vary across generational groups and are less pronounced among younger cohorts. Despite these contributions, the study has several limitations. Its cross-sectional design captures attitudes at a single point in time and does not allow causal inference or longitudinal analysis of attitude change. The use of self-reported, hypothetical measures may not fully reflect actual behaviour in real-world adoption scenarios. Additionally, online data collection may introduce self-selection bias, favouring respondents with higher digital literacy and technological interest. Overall, the findings highlight the importance of considering demographic heterogeneity when developing, communicating, and regulating autonomous vehicle technologies, while also underscoring the need for longitudinal and behaviour-based research in future studies. Full article

This study examines the pricing efficiency of the Mint Gold Certificate (ALTINS1) traded on Borsa Istanbul and its relationship with the underlying asset (gram gold), focusing on the structural break identified in the data. Analyses conducted using Mann–Kendall trend analysis, the Pettitt structural break test, Rolling Window regression, and the Threshold Error Correction Model (Threshold ECM) reveal that certificate prices have systematically decoupled from the underlying asset, creating a persistent premium exceeding 16%. The findings indicate that the risk structure of the certificate has diverged from the underlying asset, the market has become desensitized to high premium levels (asymmetric threshold effect), and prices move independently of fundamental value through a speculative feedback loop (Granger causality). The study argues that the root cause of this anomaly lies in the “Limits to Arbitrage” problem stemming from supply constraints and short-sale bans, offering new evidence on the pricing efficiency of financial innovations in emerging markets. Full article

Sustaining Ghana’s construction workforce requires more than expanding training as it requires integrating apprenticeships into a coherent skills system that links education, industry, and employability. This study tests how institutional integration, practical training, and industry collaboration jointly shape the effectiveness of apprenticeship training as a pathway to a sustainable construction workforce. Using survey data from 212 students, 36 instructors, and 129 industry and policy stakeholders, the study applies Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to validate a latent construct of Effective Apprenticeship Strategies and quantify how its components explain training and workforce outcomes. A first-order measurement SEM was estimated to examine the causal relationships between the latent strategy construct and its observed indicators across the three respondent groups within a single analytical framework. The model shows strong construct validity (CFI = 0.904; RMSEA = 0.032) and reveals that structured workplace learning, institutional support, SME engagement, and technology-oriented training are the most influential components of effective apprenticeship integration, together explaining a substantial proportion of variance in apprenticeship quality and workforce readiness. The results further reveal a highly gender-polarised training pipeline (87.7% of students and 94.4% of instructors are male), indicating that current apprenticeship structures risk constraining Ghana’s future skilled labour supply and undermining long-term workforce sustainability. The study demonstrates that apprenticeship integration is not merely a training reform but a workforce sustainability mechanism. By empirically identifying which integration strategies matter most and showing how gender exclusion limits future labour capacity, the study provides a quantitative basis for redesigning Ghana’s apprenticeship system toward a more inclusive, industry-aligned, and sustainable construction workforce. Full article

Extreme natural disasters raise a fundamental question for biologically rigid food production systems: does post-disaster productivity recovery stem from technological change or from adaptive reorganization within existing constraints? In inland aquaculture, where biological processes, fixed production cycles, and capital requirements severely limit short-run technological upgrading, this distinction is particularly critical. Using two post-earthquake time points (2023 and 2025), the analysis documents productivity and efficiency patterns rather than causal recovery trajectories. Accordingly, the analysis is explicitly descriptive and does not attempt to identify causal recovery mechanisms or long-run productivity dynamics. Adaptive efficiency is not directly measured in this study; rather, the term is used as an interpretative construct to describe efficiency changes that are consistent with adaptive behavior under post-disaster constraints. This study examines productivity patterns observed during the post-earthquake period in inland trout aquaculture following the 6 February 2023 earthquake in Eastern Türkiye, with a particular focus on adaptive efficiency as a recovery-consistent mechanism. Using a balanced panel of 290 inland trout farms observed during the immediate post-earthquake adjustment period (2023) and a subsequent recovery phase (2025), the analysis integrates bias-corrected Data Envelopment Analysis, Malmquist productivity decomposition, and resilience-oriented truncated regression. Recovery dynamics are examined conditional on farm survival, allowing within-farm adaptive adjustment to be distinguished from exit-driven selection effects. The results indicate that productivity recovery was driven predominantly by improvements in technical efficiency, while technological change remained close to unity across provinces, suggesting short-run production frontier stability. This pattern is consistent with delayed or constrained investment behavior under heightened uncertainty rather than with technological stagnation. This interpretation is not unique and should be read as one plausible mechanism among several, rather than as a definitive explanation of observed frontier stability. Farms primarily restored performance through operational reorganization, input coordination, and scale adjustment within existing biological and technological constraints, rather than through innovation. Second-stage results further show that the coefficient on access to liquidity is positive, while higher mortality rates and greater distance to markets are systematically associated with weaker post-disaster adjustment. Overall, the findings indicate that short- to medium-term productivity patterns in biologically rigid inland aquaculture systems are governed primarily by efficiency changes consistent with adaptive efficiency rather than technological change. From a policy perspective, post-disaster aquaculture recovery strategies should prioritize liquidity support, biological continuity, and operational stability over premature technology-push interventions. The analysis is based on two post-disaster observation points (2023 and 2025), which allows identification of short- to medium-term recovery-consistent patterns but does not permit causal or long-run inference. Full article