A Square Peg in a Round Hole? Challenges with DALY‐based “Burden of Disease” Calculations in Surgery and a Call for Alternative Metrics
Bibliographic record
Abstract
INTRODUCTION: In recent years, surgical providers and advocates have engaged in a growing effort to establish metrics to estimate capacity for surgical services as well the burden of surgical diseases in resource-limited settings. The burden of disease (BoD) studies have established the disability-adjusted life year (DALY) as the primary metric to measure both disability and premature mortality. Nonetheless, DALY-based approaches present methodological challenges, some of which are unique to surgical conditions, not fully addressed through the multiple iterations of the BoD studies, including the most recent study. METHODS AND RESULTS: This paper examines these challenges in detail, including issues around age-weighting and discounting, and estimates of disability-weights for specific conditions. Surgical burden measurements of specific conditions, or through the assessment of hospital wards as platforms for service delivery, still have unresolved methodological hurdles. The 2010 BoD study addresses some of these issues, but many questions still remain. Other methods estimating surgical prevalence, backlogs in treatment, and disability incurred by delays in care may provide more practical approaches to disease burden that can be useful tools for clinicians and health advocates. CONCLUSIONS: These approaches warrant further exploration in LMICs and these debates require active engagement by surgical providers and advocates globally.
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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 | 0.211 | 0.379 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".