Surgical density and its effect on esophageal cancer (EC) and gastric (GC) mortality.
Bibliographic record
Abstract
16 Background: Surgical resection plays an integral role in the multimodality treatment of patients with EC or GC. The distribution of thoracic and general surgeons at the county level varies widely across the US. The impact of the allocation of these surgeons on cancer outcomes is unclear. Our aims were to 1) examine the effect of surgeon density on EC or GC mortality, 2) compare the relative roles of thoracic and general surgeons on EC and GC outcomes and 3) determine other county characteristics associated with cancer mortality. Methods: Using county-level data from the Area Resources File, U.S. Census and National Cancer Institute, we constructed regression models to explore the effect of thoracic and general surgeon density on EC and GC mortality, respectively. Multivariate analyses controlled for incidence rate, county demographics (population aged 65+, proportion eligible for Medicare, education attainment, metropolitan vs. rural), socioeconomic factors (median household income) and healthcare resources (number of general practitioners, number of hospital beds). Results: In total, 332 and 402 counties were identified for EC and GC, respectively: mean EC/GC incidence = 5.29/6.83; mean EC/GC mortality=4.70/3.92; 91% were metropolitan and 9% were rural; mean thoracic and general surgeon densities were 10 and 63 per 100,000 people, respectively. When compared to counties with no thoracic surgeons, those with at least 1 thoracic surgeon had reduced EC mortality (beta coefficient -0.031). For GC, counties with 1 or more general surgeons also had decreased number of deaths (beta coefficient -0.095) when compared with those without any surgeons. While increasing the density of surgeons beyond 10 only yielded minimal improvements in EC mortality, it resulted in significant further reductions in GC mortality. Other county characteristics, such as increased number of hospital beds and higher median household income, were correlated with improved outcomes. Conclusions: Mortality from GC appears to be more susceptible to the benefits of increased surgeon density. For EC, a strategic policy of allocating health resources and distributing the workforce across counties will be best able to optimize outcomes at the population-level. No significant financial relationships to disclose.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".