Pediatric Ischemic Stroke: Clinical and Paraclinical Manifestations—Algorithms for Diagnosis and Treatment
, Mariana Sprincean 3,4,*, Ludmila Sidorenko 3, Ninel Revenco 4 and Svetlana Hadjiu 4Round 1
Reviewer 1 Report
Comments and Suggestions for Authors1. Abstract: The abstract seems somewhat rambling and redundant and would benefit from editing.
2. Materials and Methods, page 2, line 87: Are these patients from a single center? This seems like a lot of pediatric strokes for one center. What is the overall population from which this is drawn?
3. Materials and Methods, page 2, line 94: What was the control group for the logistic regression?
4. Results, page 3, line 104: How was stroke diagnosed in these patients? Did they undergo an imaging study (e.g., MRI, CT) or is it based on clinical presentation?
5. Results, table 1: How is irascibility defined? All newborns exhibit some irascibility.
6. Results, page 3, line 128: What was the dichotomous dependent variable in the logistic regression?
7. Results, table 2: Why did the authors create a table for the youngest age group, but then only briefly discuss the other age groups in the paper. It would be more useful to include all of the age groups in the table and then point out differences between the groups.
8. Results, page 6: It is still not clear what is being compared in the logistic regression and what the odds ratios refer to.
9. Results, page 6, line 189: What is table 4.3?
10. Discussion, page 7, line 243: What are the time-dependent treatments for pediatric stroke patients?
11. Discussion, page 8, line 268: What is loke CT?
12. Discussion: Why are there so many algorithms? It would seem like they could be condensed into a single algorithm.
Comments on the Quality of English LanguagePaper will require extensive English language editing.
Author Response
Please see attachment
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsSpecify the data sources (e.g., "Using retrospective data from case histories and prospective data from recent cases...")
Clarify the outcome measures (e.g., "To develop and validate algorithms that can predict...")
State the potential impact on clinical practice (e.g., "To facilitate timely intervention for childhood stroke, reducing treatment delays...")
Mention how the study's findings will be utilized (e.g., "To implement the algorithms in clinical settings for real-time decision support...")
The methods section is extremely small. It would be advisable for the authors to include much more in the study's methods section to allow others to replicate or reproduce the study. This would involve specifying the process of data collection, variables considered, detailed statistical modeling, validation techniques for the algorithms, and any ethical protocols followed during the study. Hence, to ensure replicability and reproducibility, the study methods section should include the following information:
1. **Population and Sample Size**: Clear description of the population from which the sample was drawn, including inclusion and exclusion criteria, and how the sample size was determined.
2. **Data Collection Procedures**: Detailed explanation of how the data was collected, including specific timeframes, settings, and data sources (e.g., medical records, diagnostic tools).
3. **Analytical Techniques**: Precise description of the statistical methods and algorithms used, including logistic regression analysis, criteria for variable selection, and any software or tools utilized.
4. **Algorithm Development**: A step-by-step process on how the algorithms were developed, tested, and validated, as well as any machine learning techniques applied.
5. **Ethical Considerations**: Information on how ethical approval was obtained, and how participant confidentiality and data protection were ensured.
Clearly articulate certain points in your discussion section to offer readers a comprehensive understanding of the research context and to help guide future work.
Improvements in discussing strengths and limitations:
1. **Strengths:**
- Emphasizing the novelty of the developed algorithms.
- Highlighting the use of a robust dataset that spans a significant number of years and combines retrospective and prospective data.
- Detailing how the study addresses a gap in clinical practice for pediatric stroke diagnosis.
2. **Limitations:**
- Acknowledging potential biases in retrospective data collection.
- Discussing the generalizability of the results to different populations or health care settings.
- Considering the limitations of logistic regression and machine learning models, such as overfitting or the need for external validation.
- Identifying any limitations in the data collection methods or the completeness of the data.
A well-rounded discussion section would consider the methodological constraints, potential biases, the representativeness of the sample, the external validity of the findings, and how these factors might influence the interpretation and applicability of the study's conclusions. The authors should also suggest directions for future research based on these reflections.
Convince us, the readers, that the statistical methods used are appropriate for the study design and that the analysis has been correctly conducted and interpreted.
Interpret the results in the context of the existing literature, and any contradictions or confirmations with previous research should be discussed.
It should be mentioned that the conclusions regarding causality are speculative and that further research might be needed to establish causative links.
Conclusions about the broader population should be limited to what can be supported by the sample studied.
Identify any potential confounders that might have been present and discuss how they were controlled for or how they might impact the results.
Acknowledge all relevant limitations in the study design and data, and consider how these might affect the strength of the conclusions.
Graphics should be inserted in sections where they contribute most effectively to the understanding of the text. It is important that the graphics are placed correctly to maintain the clarity and coherence of the report: Graphics that illustrate the background or context of the study should be included in the introduction. Graphics that describe the approach, procedures, or experimental setup should be in the methods section. Graphics that display the data collected, such as tables, charts, or images of results, should be in the results section. To address this issue, the authors should revise the paper and relocate the graphics to the appropriate sections. Furthermore, the captions of the graphics should clearly describe what the graphic represents and how it relates to the text, which should help to place them in the correct sections.
The quality of graphics in an academic article can significantly affect both the perceived professionalism of the work and the clarity of the data being presented. Professional, well-designed graphics help in effectively communicating complex information and ensure that the study's findings are accessible to readers. Your graphics appear amateurish, this could potentially distract from the credibility of the research and make it harder for other scientists to interpret the data. The authors should seek assistance from someone with expertise in graphic design, particularly in a scientific context. Alternatively, the authors might consider training in the use of graphic design tools and principles of visual communication in science.
Comments on the Quality of English Languagen/a
Author Response
Please see attachment
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsPaper is rather long but I believe that the authors have adequately addressed reviewer questions.
Comments on the Quality of English LanguagePaper is improved but some English language editing still required
Reviewer 2 Report
Comments and Suggestions for AuthorsThank you for addressing my comments from the first round. I have no additional feedback to provide in the second round.
