Financial contributions to global surgery: an analysis of 160 international charitable organizations
Bibliographic record
Abstract
BACKGROUND: The non-profit and volunteer sector has made notable contributions to delivering surgical services in low-and middle-income countries (LMICs). As an estimated 55 % of surgical care delivered in some LMICs is via charitable organizations; the financial contributions of this sector provides valuable insight into understanding financing priorities in global surgery. METHODS: Databases of registered charitable organizations in five high-income nations (United States, United Kingdom, Canada, Australia, and New Zealand) were searched to identify organizations committed exclusively to surgery in LMICs and their financial data. For each organization, we categorized the surgical specialty and calculated revenues and expenditures. All foreign currency was converted to U.S. dollars based on historical yearly average conversion rates. All dollars were adjusted for inflation by converting to 2014 U.S. dollars. RESULTS: One hundred sixty organizations representing 15 specialties were identified. Adjusting for inflation, in 2014 U.S. dollars (US$), total aggregated revenue over the years 2008-2013 was $3·4 billion and total aggregated expenses were $3·1 billion. Twenty-eight ophthalmology organizations accounted for 45 % of revenue and 49 % of expenses. Fifteen cleft lip/palate organizations totaled 26 % of both revenue and expenses. The remaining 117 organizations, representing a variety of specialties, accounted for 29 % of revenue and 25 % of expenses. In comparison, from 2008 to 2013, charitable organizations provided nearly $27 billion for global health, meaning an estimated 11.5 % went towards surgery. CONCLUSION: Charitable organizations that exclusively provide surgery in LMICs primarily focus on elective surgeries, which cover many subspecialties, and often fill deep gaps in care. The largest funding flows are directed at ophthalmology, followed by cleft lip and palate surgery. Despite the number of contributing organizations, there is a clear need for improvement and increased transparency in tracking of funds to global surgery via charitable organizations.
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.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".