Modeling the Scale‐up of Surgical Services for Children with Surgically Treatable Congenital Conditions in Somaliland
Bibliographic record
Abstract
BACKGROUND: Congenital conditions comprise a significant portion of the global burden of surgical conditions in children. In Somaliland, over 250,000 children do not receive required surgical care annually, although the estimated costs and benefits of scale-up of children's surgical services to address this disease burden is not known. METHODS: We developed a Markov model using a decision tree template to project the costs and benefits of scale-up of surgical care for children across Somaliland. We used a proxy set of congenital anomalies across Somaliland to estimate scale-up costs using three different scale-up rates. The cost-effectiveness ratio and net societal monetary benefit were estimated using these models, supported by disability weights in existing literature. RESULTS: Overall, we found that scale-up of surgical services at an aggressive rate (22.5%) over a 10-year time horizon is cost effective. Although the scale-up of surgical care for most conditions in the proxy set was cost effective, scale-up of hydrocephalus and spina bifida are not as cost effective as other conditions. CONCLUSIONS: Our analysis concludes that it is cost effective to scale-up surgical services for congenital anomalies for children in Somaliland.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".