Burden, need, or backlog: A call for improved metrics for the global burden of surgical disease
Bibliographic record
Abstract
The global burden of disease (GBD) has been measured primarily through the use of the DALY metric. Using this approach, preliminary estimates were that 11% of the GBD is surgical. However, prior work has questioned specific aspects of the GBD methodology as well as its practicality. This paper refines other conceptual approaches based on met and unmet population need for services by considering incident and prevalent need as well as backlogs for treatment that can inform effective coverage of services. Some of these methods are tested using the example of surgical repair of cleft lip and palate. Measurement of disability incurred by delays in care may also be estimated through these approaches and has not previously been estimated through a validated model. These concepts may provide more practical information for individuals and organizations to advocate for scaling up surgical programs. While many surgical conditions are unique, as a single intervention can lead to cure, these concepts may also prove useful for non-surgical diseases. Further exploration of these approaches is merited in resource-limited settings.
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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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.021 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".