Level of Evidence in Minimal Access Pediatric Surgery
Bibliographic record
Abstract
INTRODUCTION: The use of minimal access techniques is rapidly expanding in pediatric surgery. Our aim was to answer two questions: (1) What is the current quality of evidence for minimal access pediatric surgery (MAPS)? and (2) Has the evidence for MAPS improved with respect to focus and methodology over a 12-year period (1995-2006)? METHODS: A systematic review was performed. Data collected included: study characteristics, methods, and outcomes recorded. Approval by a research ethics board (REB) was recorded, where applicable, and articles were assessed for the reporting of learning curves and study limitations. Studies were divided into two eras according to publication date. Data were compared by using correlation, chi-squares, and univariate analyses. RESULTS: Four hundred and ten studies met the inclusion criteria. Of those, 260 (63.4%) were published in the late era. Only 1.46% of studies were level 1, whereas level 4 evidence was predominant (71.46%). The two eras were comparable with regard to country of origin, single-institution studies, length of follow-up, and quality of outcomes reporting. More studies reported REB approval (P = 0.0001) and clearly documented limitation of study design (P = 0.03) in the late era. CONCLUSIONS: There has been a significant increase in the number of articles dealing with MAPS. Recent studies were more likely to report limitations of study design and REB approval, but overall, there was no increase in level of evidence in the MAPS literature over the past 12 years. Although more research is being published, more attention needs to be paid to producing higher quality evidence.
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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 | 0.025 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".