Disability Weights for Pediatric Surgical Procedures: A Systematic Review and Analysis
Bibliographic record
Abstract
BACKGROUND: Metrics to measure the burden of surgical conditions, such as disability weights (DWs), are poorly defined, particularly for pediatric conditions. To summarize the literature on DWs of children's surgical conditions, we performed a systematic review of disability weights of pediatric surgical conditions in low- and middle-income countries (LMICs). METHOD: For this systematic review, we searched MEDLINE for pediatric surgery cost-effectiveness studies in LMICs, published between January 1, 1996, and April 1, 2017. We also included DWs found in the Global Burden of Disease studies, bibliographies of studies identified in PubMed, or through expert opinion of authors (ES and HR). RESULTS: Out of 1427 publications, 199 were selected for full-text analysis, and 30 met all eligibility criteria. We identified 194 discrete DWs published for 66 different pediatric surgical conditions. The DWs were primarily derived from the Global Burden of Disease studies (72%). Of the 194 conditions with reported DWs, only 12 reflected pre-surgical severity, and 12 included postsurgical severity. The methodological quality of included studies and DWs for specific conditions varied greatly. INTERPRETATION: It is essential to accurately measure the burden, cost-effectiveness, and impact of pediatric surgical disease in order to make informed policy decisions. Our results indicate that the existing DWs are inadequate to accurately quantify the burden of pediatric surgical conditions. A wider set of DWs for pediatric surgical conditions needs to be developed, taking into account factors specific to the range and severity of surgical conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".