Association between Surgery, Anesthesia, and Obstetric Workforce and Emergent Surgical and Obstetric Mortality among United States Hospital Referral Regions
Bibliographic record
Abstract
OBJECTIVE: To determine the association between SAO workforce and mortality from emergent surgical and obstetric conditions within US HR Rs. BACKGROUND: SAO workforce per capita has been identified as a core metric of surgical capacity by the Lancet Commission on Global Surgery, but its utility has not been assessed at the subnational level for a high-income country. METHODS: The number of practicing surgeons, anesthesiologists, and obstetricians per capita was estimated for all HRRs using the US Health Resources & Services Administration Area Health Resource File Database. Deaths due to emergent general surgical and obstetric conditions were determined from the Center for Disease Control and Prevention WONDER database. We utilized B-spline quantile regression to model the relationship between SAO workforce and emergent surgical mortality at different quantiles of mortality and calculated the expected change in mortality associated with increases in SAO workforce. RESULTS: The median SAO workforce across all HRRs was 74.2 per 100,000 population (interquartile range 33.3-241.0). All HRRs met the Lancet Commission on Global Surgery lower target of 20 SAO per 100,000, and 97.7% met the upper target of 40 per 100,000. Nearly 2.8 million Americans lived in HRRs with fewer than 40 SAO per 100,000. Increases in SAO workforce were associated with decreases in surgical mortality in HRRs with high mortality, with minimal additional decreases in mortality above 60 to 80 SAO per 100,000. CONCLUSIONS: Increasing SAO workforce capacity may reduce emergent surgical and obstetric mortality in regions with high surgical mortality but diminishing returns may be seen above 60 to 80 SAO per 100,000. Trial Registration: N/A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".