Key Concepts for Estimating the Burden of Surgical Conditions and the Unmet Need for Surgical Care
Bibliographic record
Abstract
BACKGROUND: Surgical care is emerging as a crucial issue in global public health. Methodology is needed to assess the impact of surgical care from a public health perspective. METHODS: A consensus opinion of a group of surgeons, anesthesiologists, and public health experts was established regarding the methodology for estimating the burden of surgical conditions and the unmet need for surgical care. RESULTS: For purposes of analysis, we define surgical conditions as any disease state requiring the expertise of a surgically trained provider. Abnormalities resulting from a surgical condition or its treatment are termed surgical sequelae. Surgical care is defined as any measure that reduces the rates of physical disability or premature death associated with a surgical condition. To measure the burden of surgical conditions and unmet need for surgical care we propose using cumulative disability-adjusted life-year (DALY) curves generated from age-specific population-based data. This conceptual framework is based on the premise that surgically associated disability and death is determined by the incidence of surgical conditions and the quantity and quality of surgical care. The burden of surgical conditions is defined as the total disability and premature deaths that would occur in a population should there be no surgical care; the unmet need for surgical care is defined as the potentially treatable disability and premature deaths due to surgical conditions. Burden of surgical conditions should be expressed as DALYs and unmet need as potential DALYs avertable. CONCLUSIONS: Methodology is described for estimating the burden of surgical conditions and unmet need for surgical care. Using this approach it will be feasible to estimate the global burden of surgical conditions and help clarify where surgery fits among other global health priorities. These methods need to be validated using population-based data.
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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 | 0.068 | 0.144 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".