Surgical Non‐governmental Organizations: Global Surgery’s Unknown Nonprofit Sector
Bibliographic record
Abstract
BACKGROUND: Charitable organizations may play a significant role in the delivery of surgical care in low- and middle-income countries (LMICs). However, in order to quantify their collective contribution, to account for the care they provide in national surgical plans, and to maximize coordination between organizations, a comprehensive database of these groups is required. We aimed to create such a database using web-available data. METHODS: We searched for organizations that meet the United Nations Rule of Law definition of non-governmental organizations and provide surgery in LMICs. We termed these surgical non-governmental organizations (s-NGOs). We screened multiple sources including a listing of disaster relief organizations, medical volunteerism databases, charity commissions, and the results of a literature search. We performed a secondary review of each eligible organization's website to verify inclusion criteria and extracted data. RESULTS: We found 403 s-NGOs providing surgery in all 139 LMICs, with most (61 %) incorporating surgery into a broader spectrum of health services. Over 80 % of s-NGOs had an office in the USA, the UK, Canada, India, or Australia, and they most commonly provided surgery in India (87 s-NGOs), Haiti (71), Kenya (60), and Ethiopia (55). The most common specialties provided were general surgery (184), obstetrics and gynecology (140), and plastic surgery (116). CONCLUSIONS: This new catalog includes the largest number of s-NGOs to date, but this is likely to be incomplete. This list will be made publicly available to promote collaboration between s-NGOs, national health systems, and global health policymakers.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".