The Global Burden of Surgical Disease: An Analysis on Inaccessible Surgical Care in Low and Middle Income Countries
Bibliographic record
Abstract
Worldwide, 4.8 billion people do not have access to safe, adequate surgical care and anaesthetic management. Surgical care has been deemed “the neglected child of global health,” a startling reminder of the disparities in health services. The provision of surgical interventions can avert 11% of the global burden of disease and 1.5 million deaths each year. Many obstacles exist for low- and middle-income countries (LMIC) to progress towards accessible surgical care. The first challenge is delivering cost-effective surgical care despite financial constraints and political turmoil. Foreign aid was established to alleviate the financial burden and its contributions have been pivotal. However, based on the political climate in certain countries, funds are siphoned to government sectors other than health care. Moreover, the lack of infrastructure, equipment, and personnel in LMIC compound the issue. The other challenge is determining if surgery is as feasible and effective as non-surgical health interventions. Surgical care is crucial and this paper aims to assess the challenges that limit its stature in global health discussions. The paper will address the influence of financing, infrastructure, workforce, service delivery, and information management on surgical care, and the current resolutions, such as humanitarian aid missions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".