GAPS II: Development and Pilot Results of the Global Assessment in Pediatric Surgery, an Evidence-Based Pediatric Surgical Capacity Assessment Tool for Low-Resource Settings
Bibliographic record
Abstract
Abstract PURPOSE: Pediatric surgical care in low- and middle-income countries is often hindered by systemic gaps in healthcare resources, infrastructure, training, and organisation. This study aims to develop and validate the Global Assessment of Pediatric Surgery (GAPS) to appraise pediatric surgical capacity and discriminate between levels of care across diverse healthcare settings. METHODS: The GAPS Version 1 was constructed through a synthesis of existing assessment tools and expert panel consultation. The resultant GAPS Version 2 underwent international pilot testing. Construct validation categorized institutions into providing Basic or Advanced Surgical Care. GAPS was further refined to Version 3 to include only questions with a > 75% response rate and those that significantly discriminated between Basic or Advanced Surgical settings. RESULTS: GAPS Version 1 included 139 items, which, after expert panel feedback, was expanded to 168 items in Version 2. Pilot testing, in 65 institutions yielded a high response rate. Of the 168 questions in GAPS Version 2, 64 significantly discriminated between Basic and Advanced Surgical Care. The refined GAPS Version 3 tool comprises 64 questions: Human Resources (9), Material Resources (39), Outcomes (3), Accessibility (3), and Education (10). CONCLUSION: The GAPS Version 3 tool presents a validated instrument for evaluating pediatric surgical capabilities in low-resource settings.
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".