Addressing barriers to evidence-based medicine in pediatric surgery: an introduction to the Canadian Association of Paediatric Surgeons Evidence-Based Resource
Bibliographic record
Abstract
Background: Pediatric surgical practice lags behind medicine in presence and use of evidence, primarily due to time constraints of using existing tools that are not specific to pediatric surgery, lack of sufficient patient data and unstructured pediatric surgery training methods. Method: We developed, disseminated and tested the effectiveness of an evidence-based resource for pediatric surgeons and researchers that provides brief, informative summaries of quality-assessed systematic reviews and meta-analyses on conflicting pediatric surgery topics. Results: Responses of 91 actively practicing surgeons who used the resource were analysed. The majority of participants found the resource useful (75%), improved their patient care (66.6%), and more than half (54.2%) found it useful in identifying research gaps. Almost all participants reported that the resource could be used as a teaching tool (93%). Conclusion: Lack of awareness of the resource is the primary barrier to its routine use, leading to potential calls for more active dissemination worldwide. Users of the Canadian Association of Paediatric Surgeons Evidence-Based Resource find that the summaries are useful, identify research gaps, help mitigate multiple barriers to evidence-based medicine, and may improve patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.127 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".