Associations between county-level general surgeon (GS) and gastroenterologist (GA) density and outcomes for hepatobiliary cancer (HBC).
Bibliographic record
Abstract
292 Background: Surgical resection is the mainstay of treatment for early, localized HBC. Prior studies consistently show an association between procedure volumes and cancer outcomes, but the impact of surgeon and physician density is unclear. Our aims were to 1) examine the effects of GS and GA density on HBC mortality and 2) compare the relative importance of GS versus GA density on HBC outcomes. Methods: Using county-level data from the Area Resource File, US Census, and National Cancer Institute, we developed both multivariate linear and logistic regression models to determine the effect of GS and GA density on overall HBC mortality between 2002 and 2006, while controlling for cancer incidence, county demographics and socioeconomic factors. Results: In total, 793 counties were analyzed: mean HBC incidence and mortality were 5.89 and 5.34 per 100,000 persons, respectively; 77% were metropolitan; mean GS and GA densities were 10.6 and 3.5 per 100,000 people, respectively. When compared to counties with no GS, those with at least one had a statistically significant decrease in HBC-specific mortality (beta coefficient -.115; p=.001). In contrast, when compared to counties with no GA, those with at least one showed a trend towards lower mortality (beta coefficient -.0677; p=.065). Increasing the county-level density of GS and GA improved outcomes, but increases beyond 10 GS or 4 GA per 100,000 people did not continue to result in significant reductions in HBC mortality; rather, these showed an increase in HBC mortality. Conclusions: Reductions in HBC mortality are more strongly influenced by increasing GS than GA density. There appears to be a ceiling effect at which point increasing GS and GA density does not appear to result in improvements in HBC outcomes. A strategy of allocating healthcare resources and distributing the workforce across counties will optimize outcomes at the population-level.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".