Surgical Non‐governmental Organizations: Global Surgery’s Unknown Nonprofit Sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 it