Search Results (2,114)

Background: Native joint septic arthritis is a severe infection associated with considerable morbidity. The data about the microbiological spectrum, treatment methods, and long-term outcomes are heterogeneous. Methods: We performed a decade-long retrospective study encompassing all patients with native joint septic arthritis treated at our institution, a tertiary orthopedic center. Data on demographics, clinical parameters, microbiology, surgical interventions, and antibiotic use were gathered. Outcomes included reoperation, persistent infection and mortality during follow-up. We used logistic regression to identify predictors of adverse outcomes, and Kaplan–Meier analyses to evaluate reoperation-free survival among microbiologic groups. Results: A total of 114 patients (103 adults and 11 children) were included. Cultures yielded positive results in 72 out of 103 (70%) adults and 8 out of 11 (73%) children. Staphylococcus aureus was the primary pathogen in adults (49% of positives) and children (88%), followed by coagulase-negative staphylococci. Antibiotics were administered to all patients, with combinations of at least two molecules in 68% of adults and 91% of children, while surgical intervention predominantly consisted of debridement alone. In adults, an elevated preoperative white blood cell count was associated with unfavorable outcomes in univariate analysis (odds ratio 1.14, 95% confidence interval 1.01–1.30, p = 0.040). The Kaplan–Meier analysis revealed no significant differences in reoperation-free survival across microbiologic groups (log-rank p = 0.361). Conclusions: Over a ten-year period, Staphylococcus aureus remained the predominant cause of native joint septic arthritis; however, culture-negative cases and coagulase-negative staphylococci were also common. Only preoperative leukocytosis was a predictor of poor outcomes, while microbiologic etiology did not significantly influence the risk of reoperation, potentially indicating early and effective therapy. These findings highlight the intricacy of native joint septic arthritis and the necessity for enhanced diagnostics and prognostic stratification. Full article

Background: Cardiac involvement is a major cause of morbidity and mortality in systemic sclerosis (SSc). Autoantibodies may help identify patients at increased cardiovascular (CV) risk. This systematic review aimed to assess the predictive value of classical and emerging SSc-related autoantibodies for cardiac involvement and their integration with imaging and cardiac biomarkers. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, Scopus, and the Cochrane Library up to 16 July 2025. Studies were included if they reported associations between specific autoantibodies and cardiac outcomes (e.g., myocardial fibrosis, conduction abnormalities, arrhythmias, ventricular dysfunction) in adult patients with SSc. Data extraction and quality assessment followed PRISMA 2020 guidelines. The review protocol was registered in PROSPERO (registration ID: CRD420251107782). Results: Anti-topoisomerase I antibodies were associated with myocardial fibrosis, subclinical systolic and diastolic dysfunction, elevated cardiac biomarkers, and pathological findings on cardiac magnetic resonance imaging. Anti-centromere antibodies were linked to conduction system abnormalities, particularly among older individuals. Anti-RNA polymerase III and anti-U3 ribonucleoprotein antibodies correlated strongly with arrhythmias and pericardial involvement. Novel autoantibodies, such as anti-heart antibodies and anti-intercalated disk antibodies, were linked to early myocardial injury, although their clinical utility requires further validation. Across studies, serological markers alone were insufficient to predict cardiac outcomes without concurrent imaging or biomarker evaluation. Conclusions: Autoantibody profiling plays an important role in CV risk stratification in SSc. Combining serological testing with cardiac biomarkers and advanced imaging enhances early detection and supports individualized monitoring. Further longitudinal studies are needed to validate predictive models and optimize patient outcomes. Full article

Background: Disorders of gut–brain interaction (DGBI) have a significant impact on the quality of life of children and families. Forty percent of children with recurrent abdominal pain continue to have symptoms into adulthood. Specialized programs for the transition of adolescents with DGBI to adult care are scarce. There are no widely accepted guidelines for transition of care. Identifying risk factors for persistence of symptoms into adulthood is key to identifying the optimal population that should be part of such programs and guidelines design. Methods: A narrative comprehensive review was conducted using predefined keywords to identify risk factors for persistent DGBI in children/adolescents. Results: Female sex, psychological distress, family history of DGBI, and certain comorbidities had stronger evidence for persistence, whereas other risk factors rely on limited data. Conclusions: It is suggested that transition programs should focus on adolescents presenting with multiple coexisting risk factors. The program should at least include pediatric and adult neurogastroenterologists, dieticians, psychologists, and social workers. Tertiary prevention through psychological support, school-based programs, and management of anxiety and sleep disturbances may reduce the persistence of symptoms. Prospective studies should refine risk stratification and guide transition strategies. Full article

Multidrug-resistant Gram-negative bacteremia (MDR-GNB) is a significant health threat associated with increased morbidity and mortality rates. Patients with cancer are particularly vulnerable to MDR-GNB due to immunosuppression and frequent healthcare exposure. The aim of this study was to evaluate risk factors, 30-day mortality, and outcomes in cancer and non-cancer patients. We conducted a retrospective study of adult patients aged 18 years or older with MDR-GNB who were hospitalized at Ochsner LSU Health—Academic Medical Center between January 2018 and July 2022. We collected data about demographics, comorbidities, cancer diagnosis, causative organisms, infection source, antibiotic therapy, and clinical outcomes. A total of 112 patients with MDR-GNB were included, where 31 patients (27.7%) had cancer and 81 patients (72.3%) did not. Cancer patients were more frequently male and white (74.2% vs. 58.0%, p = 0.114 and 45.2% vs. 25.9%, p = 0.031). Diabetes mellitus was more common in non-cancer patients, but it was associated with increased mortality risk in the cancer group (OR = 2.39, 95% CI: 1.125–5.074). Enterobacteriaceae species were the most frequently isolated organisms (83.0%), with no significant difference between groups. The most common source of infection was genitourinary (49.1%). ICU admission was more frequent in non-cancer patients (49.4% vs. 25.8%, p = 0.024). However, cancer patients had a higher ICU admission mortality risk (OR 2.156, 95% CI: 1.058–4.395) and recent hospitalization rates (67.7% vs. 40.7%, p = 0.011), both associated with increased mortality risk. Cancer patients had a significantly higher 30-day mortality rate (39.0% vs. 16.4%, p = 0.017; OR = 3.012, 95% CI: 1.190–7.622) and hospice admissions (22.6% vs. 3.7%, p = 0.002; OR = 7.583, 95% CI: 1.819–31.618). These findings emphasize the urgent need for early microbiological identification, targeted antimicrobial therapy, and improved infection control strategies. Given the rising prevalence of MDR-GNB pathogens, future research is needed for prompt appropriate antibacterial therapy based on risk stratification and enhanced antimicrobial stewardship programs as these are critical for high-risk cancer patients. Full article

Background: Critical Incident Reporting Systems (CIRS) play a key role in improving patient safety but facess limitations due to the unstructured nature of narrative data. Systematic analysis of such data to identify latent risk patterns remains challenging. While artificial intelligence (AI) shows promise in healthcare, its application to CIRS analysis is still underexplored. Methods: This study presents a transformer-based approach to classify incident reports into predefined risk categories and support clinical risk managers in identifying safety hazards. We compared a traditional TF-IDF/logistic regression model with a transformer-based German BERT (GBERT) model using 617 anonymized CIRS reports. Reports were categorized manually into four classes: Organization, Treatment, Documentation, and Consent/Communication. Models were evaluated using stratified 5-fold cross-validation. Interpretability was ensured via Shapley Additive Explanations (SHAP). Results: GBERT outperformed the baseline across all metrics, achieving macro averaged-F1 of 0.44 and a weighted-F1 of 0.75 versus 0.35 and 0.71. SHAP analysis revealed clinically plausible feature attributions. Conclusions: In summary, transformer-based models such as GBERT improve classification of incident report data and enable interpretable, systematic risk stratification. These findings highlight the potential of explainable AI to enhance learning from critical incidents. Full article

Background/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but a lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalances and the inherent complexity of time-dependent events. While there are current prognostic indices for survival, they do not specifically address HT risk. This study aimed to develop and validate survival-based and traditional classification machine-learning models to predict HT in cohorts. Methods: Using a multicenter retrospective dataset (n = 1068), survival models (Cox proportional hazards, Lasso-Cox, Random Survival Forest, Gradient-boosted Cox [GBM-Cox], eXtreme Gradient Boosting [XGBoost]-Cox), and classification models (Logistic regression, Lasso logistic, Random Forest, Gradient Boosting, XGBoost) were compared. The best-performing survival models—XGBoost-Cox, Lasso-Cox, and GBM-Cox—were assessed on an independent test set (n = 92). Model sensitivity was maximized using optimal binary risk cutoff points based on Youden’s index. Results: Survival models showed superior predictive performance than classical classifiers, with XGBoost-Cox exhibiting the highest mean accuracy (85.3%), time-dependent area under the curve (0.795), sensitivity (98%), specificity (83.9%), and concordance index (0.836). Incorporating next-generation sequencing (NGS) data improved model accuracy and specificity, indicating that genetic factors improve HT prediction. Principal component analysis revealed distinct gene mutation patterns associated with HT risk, highlighting DNA-repair genes such as TP53, BLM, and RAD50. Conclusions: This study highlights the clinical value of survival-based machine-learning methods integrated with NGS data to personalize HT risk stratification for patients with FL and MZL. Full article

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

Yin Yang 1 (YY1) is a multifunctional transcription factor implicated in gene regulation, cell proliferation, and survival. While its role in breast cancer (BC) has been explored, its prognostic significance remains controversial. In this study, we evaluated nuclear YY1 expression in 276 BC tissue samples using immunohistochemistry (IHC), tissue microarrays (TMAs), and digital pathology (DP). Nuclear staining was quantified using Aperio ImageScope software, focusing on tumor regions to avoid confounding from stromal or non-tumor tissues. This selective and standardized approach enabled precise quantification of YY1 expression. Our results show elevated median YY1 expression in tumor vs. normal matched tissues (p < 0.001). The optimal cutoff for medium-intensity nuclear YY1 expression in tumor areas for overall survival (OS) was established by a receiver operating characteristic (ROC) curve (AUC = 0.718, 95% CI: 0.587–0.849, p = 0.008). In contrast, ROC curves showed no prognostic impact (AUC and p-value) for YY1 quantification in whole spots (tumor + normal). As a categorical variable, high YY1 expression was correlated with more aggressive BC features, including tumor size > 3 cm (57.7% vs. 44.2% p = 0.037), the triple-negative breast cancer (TNBC) molecular subtype (27.3% vs. 13.9% p = 0.026), and advanced prognostic stage (III) (31.8% vs. 16.7% p = 0.003), while as a continuous variable, YY1 was associated with higher histological (p = 0.003) and nuclear grades (p = 0.022). High YY1 expression was significantly associated with a reduced OS of BC patients, as shown by Kaplan–Meier curves (HR = 2.227, p = 0.002). Since YY1 was significantly enriched in TNBC, we evaluated its prognostic resolution in this subgroup. But, probably due to the small number of patients within this subset, our results were not statistically significant (HR = 1.317, 95% CI: 0.510–3.405, p = 0.566). Next, we performed multivariate Cox regression, confirming YY1 as an independent prognostic factor for overall survival (HR = 1.927, 95% CI: 1.144–3.247, p = 0.014). In order to improve prognostic value, we constructed a mathematical model derived from the multivariate Cox regression results, including YYI, AJCC prognostic stage (STA), and axillary lymph node dissection (ALN), with the following equation: h(t) = h₀(t) × exp (0.695 × YY1 + 1.103 × STA − 0.503 × ALN). ROC analysis of this model showed a better AUC of 0.915, similar sensitivity (83.3%), and much higher specificity (92%). Bioinformatic analysis of public datasets supported these findings in BC, showing YY1 overexpression in multiple cancer types and its association with poor outcomes in BC. These results suggest that YY1 may play a role in tumor progression and serve as a valuable prognostic biomarker in BC. DP combined with molecular data enhanced biomarker accuracy, supporting clinical applications of YY1 in routine diagnostics and personalized therapy. Additionally, developing a combined score based on the modeling of multiple prognostic factors significantly enhanced survival predictions, representing a practical tool for risk stratification and the guidance of therapeutic decisions. Full article

Background: Sarcoma surveillance guidelines still apply uniform imaging intervals based on tumor grade and stage that ignore histotype-specific metastatic behavior. We prospectively analyzed metastatic timing, organ tropism, and lesion burden across a real-world sarcoma cohort to generate an evidence base for risk-adapted follow-up and treatment stratification. Methods: In a prospective multicenter study, 1850 patients with suspected sarcoma were screened. SHAPEHub, a real-world-time data warehouse, captured clinicopathological variables and imaging. Adults with histologically confirmed soft-tissue or bone sarcoma (n = 295) formed the analytic cohort. Metastases were classified as synchronous (≤6 months) or metachronous (>6 months), lung-only versus multi-organ, and oligometastatic (≤5 lesions, ≤2 organs) versus polymetastatic. TTME was illustrated with Kaplan–Meier curves for the full cohort (descriptive); where subgroup comparisons are shown, log-rank tests are reported. Results: Ninety-three patients (31.5%) developed metastases after a median follow-up of 20.9 months. Metastatic risk was front-loaded: 36.6% were synchronous, and 67.8% of metachronous events occurred within year 1. The lung was the initial site in 62.4% of events, bone in 18.3%, and liver in 11.8%. Half of the lung-metastatic patients remained pulmonary-confined; the remainder followed a multi-organ route involving bone and lymph nodes. Oligometastatic spread predominated in the lung-only subgroup (61%) versus multi-organ (28%). Histotype influenced both timing and tropism: angiosarcoma and Ewing sarcoma metastasized earliest (median 3.7 and 5.0 months) and multi-organ; leiomyosarcoma and UPS were lung-dominant; Ewing sarcoma and epithelioid haemangioendothelioma were bone-tropic; and angiosarcoma was liver-tropic. Conclusions: Metastatic sarcoma displays three intersecting dimensions—early versus late onset, organ-specific tropism, and oligo- versus polymetastatic burden—none of which are addressed by the current “one-size-fits-all” surveillance. Recognizing these patterns delineates windows for tailored imaging and stratified therapy selection (e.g., local ablation for oligometastatic lung disease, intensified systemic regimens for early, polymetastatic spread). These findings lay the groundwork for precision-adapted surveillance and treatment protocols. Pattern-stratified trials and health-economic evaluations are now needed to assess whether this approach improves outcomes and optimizes resource allocation. Full article

Background: Malnutrition and systemic inflammation are major determinants of poor outcomes in hospitalized older adults, such as length of hospital stay (LOS), mortality, and readmission risk. The C-reactive protein to albumin ratio (CRP/Alb) and the Prognostic Nutritional Index (PNI) are simple biomarkers reflecting inflammation and nutritional status. Additionally, the PNI offers a straightforward method to assess both the nutritional state and mortality risk in older patients. Objective: The objective of this study was to compare the predictive accuracy of the CRP/Alb ratio and PNI for early in-hospital mortality at 7 and 30 days after admission in older patients, independent of admitting diagnosis. Methods: We retrospectively analyzed 2776 patients aged 65 years and older, admitted to the Internal Medicine and Aging Unit of the “Policlinico Riuniti” University Hospital in Foggia, Italy, between 2019 and 2025. Laboratory data at admission included CRP, albumin, and total lymphocyte count (TLC). The CRP/Alb ratio and PNI were calculated. Prognostic performance for 7- and 30-day mortality for both the CRP/Alb ratio and PNI was assessed using ROC curves, Cox regression, Kaplan–Meier survival analyses, and positive predictive value (PPV) comparisons, stratified by rehospitalization status and length of stay (LOS). The likelihood-ratio test was also performed to compare the 7- and 30-day mortality PPVs of the CRP/Alb ratio and the PNI, both for all patients and for re-hospitalized patients. Results: In-hospital mortality occurred in 444 patients (16%). Deceased patients showed significantly higher CRP/Alb ratios and lower PNI values than survivors (p < 0.001). Both the CRP/Alb ratio and PNI independently predicted 7- and 30-day mortality. A CRP/Alb ratio > 8 strongly predicted very early mortality (HR 10.46 for 7-day death), whereas a PNI < 38 predicted both 7- and 30-day mortality (HR 8.84 and HR 3.54, respectively). Among non-rehospitalized patients, the PNI demonstrated superior predictive performance regardless of LOS (p < 0.001). Among rehospitalized patients, the PNI was a more accurate predictor for short LOS (<7 days), while the CRP/Alb ratio performed better for longer LOS (≥7 days). Conclusions: Both the CRP/Alb ratio and PNI are inexpensive, readily available biomarkers for early risk stratification in hospitalized older adults. The CRP/Alb ratio is particularly effective in detecting very early mortality risk, while the PNI offers refined prognostic value across selected subgroups. Integrating these markers at admission may support personalized geriatric care and timely interventions. Full article

Risk prediction models are increasingly used in the management of coronary artery disease (CAD), with applications ranging from diagnostic stratification to prognostic assessment and therapeutic guidance. In the context of CAD and percutaneous coronary intervention, clinical decision-making often relies on risk scores to estimate the likelihood of ischemic and bleeding events and to tailor antithrombotic strategies accordingly. Traditional scores are derived from clinical, anatomical, procedural, and laboratory variables, and their performance is evaluated based on discrimination and calibration metrics. While many established models are simple, interpretable, and externally validated, their predictive ability is often moderate and may be limited by outdated derivation cohorts, overfitting, or lack of generalizability. Recent advances have introduced artificial intelligence and machine learning models that can process large, high-dimensional datasets and identify patterns not apparent through conventional methods, with the aim to incorporate complex data; however, they are not exempt from limitations and struggle with integration into clinical practice. Notably, ethical issues, such as equity in model application, over-stratification, and real-world implementation, are of critical importance. The ideal predictive model should be accurate, generalizable, and clinically actionable. This review aims at providing an overview of the main predictive models used in the field of CAD and to discuss methodological challenges, with a focus on strengths, limitations and areas of applicability of predictive models. Full article

Mild traumatic brain injury (mTBI) remains a clinical challenge, particularly in cases with normal computed tomography (CT) findings but persistent or evolving symptoms. Conventional diagnostic approaches relying solely on clinical criteria and neuroimaging often lack adequate sensitivity and may lead to unnecessary radiation exposure. Recent advances in biomarker research have identified several blood-based proteins such as glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein B (S100B), Tau protein, neuron-specific enolase (NSE), and neurofilament light chain (NFL) as potential tools for improving diagnostic precision and guiding clinical decisions. In this study, we synthesize current evidence evaluating the diagnostic and prognostic utility of these biomarkers using sensitivity, specificity, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). GFAP and UCH-L1 have shown high sensitivity in detecting intracranial lesions and are now FDA-cleared for emergency department triage within 12 h of injury. While S100B remains widely investigated, its low specificity limits its application beyond select clinical scenarios (i.e., in patients without polytrauma). Additionally, Tau, NSE, and NFL are emerging as prognostic markers, with studies suggesting associations with persistent symptoms and long-term neurocognitive outcomes. Overall, the integration of biomarker-based data into clinical workflows may enhance early mTBI diagnosis, reduce reliance on imaging, and enable individualized follow-up and prognostic stratification. Future research should refine optimal sampling windows and explore multimarker panels to maximize diagnostic and prognostic performance. Full article

Background/Objectives: Spontaneous intracranial hemorrhage (sICH) is a life-threatening condition with high in-hospital mortality rates. Prognostic evaluation remains challenging, and biomarkers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), glucose-to-lymphocyte ratio (GLR), and high-sensitivity troponin-I (hs-cTn-I) have been studied for their potential prognostic significance. This study aimed to evaluate the role of hs-cTn-I, NLR, PLR, LMR, and hematoma volume in predicting prognosis in sICH patients. Methods: This retrospective study included 49 adult patients (>18 years) admitted between January 2021 and January 2024 with sICH and hematoma volume >30 mL. All patients underwent surgery within 24 h of admission. Laboratory data, including hs-cTn-I levels and hematological indices, were collected. The hematoma volume was measured using the ABC/2 method. Patients were divided into survival and mortality groups. Statistical analyses were performed using SPSS, with p < 0.05 considered significant. Results: Of the 49 patients, 24 (49%) died. Admission hs-cTn-I levels showed no significant difference between groups, but levels on days 7 and 30 were significantly higher in the mortality group (p < 0.001). ROC analysis revealed hs-cTn-I levels on day 30 had better prognostic performance (AUC: 0.89, cut-off: 46 ng/mL, sensitivity: 76%, specificity: 88%). The hematoma volume and admission hematological indices (NLR, PLR, LMR, SII, and GLR) were not significantly associated with prognosis. Conclusions: Elevated hs-cTn-I levels, particularly on days 7 and 30, were significant predictors of in-hospital mortality in sICH patients. While admission hematological indices and hematoma volume lacked prognostic value, hs-cTn-I may serve as a valuable biomarker for risk stratification in clinical practice. Further multicenter studies with larger cohorts and multivariate analyses are needed to validate these findings. Full article

Introduction: Total hip arthroplasty is a major orthopedic intervention, which is increasingly performed, and involves a variable duration of postoperative hospitalization. Estimating this duration is essential for optimizing resources and improving perioperative planning. Materials and Methods: The retrospective study included 85 patients admitted to an orthopedic clinic between 2020 and 2025, undergoing primary hip arthroplasty. Pre- and postoperative clinical, biological, and surgical data were collected. Based on these variables, the H.I.P.P.O. (Hip Intervention Patient Prognostic Outcome) score was developed, using a Random Forest algorithm to predict the length of stay (short, medium, long). Results: The model achieved an overall accuracy of 80%. The most important predictors were as follows: day of surgery, type of prosthesis, preoperative fibrinogen, INR, APTT, preoperative hemoglobin, age, and presence of liver cirrhosis. The H.I.P.P.O. score allowed efficient stratification of patients and showed a high capacity to identify cases at risk of prolonged hospitalization (F1 score = 0.857). Conclusions: The H.I.P.P.O. score is a practical, interpretable, and clinically applicable tool that integrates biological and organizational factors to predict the length of stay after hip arthroplasty. It can support surgical decision making and optimize perioperative management. Full article

The purpose of this narrative review is to critically appraise recent advances in sports injury rehabilitation—primarily focusing on biopsychosocial (BPS) approaches alongside emerging technological innovations—and identify current gaps and future directions. A literature search was conducted in PubMed, Scopus, and Web of Science for the years 2018–2024. Eligible records were English-language, human studies comprising systematic reviews, clinical trials, and translational investigations on wearable sensors, artificial intelligence (AI), virtual reality (VR), regenerative therapies (platelet-rich plasma [PRP], bone marrow aspirate concentrate [BMAC], stem cells, and prolotherapy), and BPS rehabilitation models; single-patient case reports, editorials, and non-scholarly sources were excluded. The synthesis yielded four themes: (1) BPS implementation remains underutilised owing to a lack of validated tools, variable provider readiness, and system-level barriers; (2) wearables and AI can enhance real-time monitoring and risk stratification but are limited by data heterogeneity, non-standardised pipelines, and sparse external validation; (3) VR/gamification improves engagement and task-specific practice, but evidence is dominated by pilot or laboratory studies with scarce longitudinal follow-up data; and (4) regenerative interventions show mechanistic promise, but conclusions are constrained by methodological variability and regulatory hurdles. Conclusions: BPS perspectives and emerging technologies have genuine potential to improve outcomes, but translation to practice hinges on (1) pragmatic or hybrid effectiveness–implementation trials, (2) standardisation of data and intervention protocols (including core outcome sets and effect-size reporting), and (3) integration of psychological and social assessment into routine pathways supported by provider training and interoperable digital capture. Full article