Age at Primary Cleft Lip Repair: A Potential Bellwether Indicator for Pediatric Surgery
Bibliographic record
Abstract
The bellwether procedures described by the Lancet Commission on Global Surgery represent the ability to deliver adult surgical services after there is a clear and easily made diagnosis. There is a need for pediatric surgery bellwether indicators. A pediatric bellwether indicator would ideally be a routinely performed procedure, for a relatively common condition that, in itself, is rarely lethal at birth, but that should ideally be treated with surgery by a standard age. Additionally, the condition should be easy to diagnose, to minimize the confounding effects of delays or failures in diagnosis. In this study, we propose the age at primary cleft lip (CL) repair as a bellwether indicator for pediatric surgery. METHOD: We reviewed the surgical records of 71,346 primary cleft surgery patients and ultimately studied age at CL repair in 40,179 patients from 73 countries, treated by Smile Train partners for 2019. Data from Smile Train's database were correlated with World Bank and WHO indicators. RESULTS: Countries with a higher average age at CL repair (delayed access to surgery) had higher maternal, infant, and child mortality rates as well as a greater risk of catastrophic health expenditure for surgery. There was also a negative correlation between delayed CL repair and specialist surgical workforce numbers, life expectancy, percentage of deliveries by C-section, total health expenditure per capita, and Lancet Commission on Global Surgery procedure rates. CONCLUSION: These findings suggest that age at CL repair has potential to serve as a bellwether indicator for pediatric surgical capacity in Lower- and Middle-income Countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".