Providing paediatric surgery in low-resource countries
Bibliographic record
Abstract
Successful health systems comprise good outcomes, accessibility and availability. Surgery is the service that cuts across many treatment scenarios, yet in low- and middle-income countries 90% of people cannot access it. Estimates using most recent population data suggest that 1.75 billion children lack access to surgical care. Additionally, 30% of the global burden of disease is treatable with surgery, yet in LMICs as much as 87% of the surgical need remains unmet. Paediatric surgical services are not at the level they need to be, highlighting an increasing surgical burden on children’s health globally with a human cost of morbidity and mortality. Achieving Universal Health Coverage and the Sustainable Development Goals will fail if surgical systems are not strengthened in low resource settings. In 2018, global health charity Kids Operating Room was founded with a goal of ensuring every child has access to the surgery they need. The charity has a four-pillar approach to its work: provision of infrastructure and equipment, paediatric surgical workforce training, database development and research capacity strengthening, and advocating on behalf of children denied access to safe surgery. To ensure that paediatric surgical interventions produce real impact on service delivery, contextual understanding and needs assessment are key. The building of paediatric surgical capacity should align to countries’ priorities and wishes. Investing in local health workforce is essential to delivering quality services, supporting resilient health systems and provides integrated, people-centred health services. A competent surgical information system gives the local surgical workforce the tools needed to action evidence-driven decisions. Strengthening surgical services in a manner aligned to the WHO’s fundamental health system building blocks, allows for sustainable and long-lasting change. Confronting bottlenecks that exist in surgical services and establishing multi-faceted development, will allow global, national and local surgical targets to be met.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".