Associations between county-level surgeon density and colorectal cancer (CRC) mortality.
Bibliographic record
Abstract
511 Background: Strong associations between surgeon procedure volumes and patient outcomes have been observed for many types of cancers. Whether surgeon density in a population has a similar impact on cancer outcomes is unclear. Our aims were to 1) explore the effect of US county-level surgeon density on CRC mortality and on annual changes in death, 2) compare the relative importance of colorectal surgeon (CS) versus general surgeon (GS) density on these CRC outcomes, and 3) identify other county characteristics associated with reduced mortality. Methods: Using county-level data from the Area Resource File, US Census and National Cancer Institute, we developed multivariate regression models to determine the effect of a) CS and b) GS on overall CRC mortality and changes in death between 2002 and 2006, while controlling for CRC incidence, county demographics and other socioeconomic factors. Results: A total of 1,187 US counties were included: mean CRC incidence and death rates were 64.9 and 19.9, respectively; 57% were metropolitan and 43% were rural counties; mean CS and GS densities were 1.23 and 1.94 per 100,000 people, respectively. When compared to counties with no CS and no GS, those with at least of one of these surgeons had a statistically significant decrease in CRC-specific mortality (beta coefficients were -0.035 and -0.051 for CS and GS, respectively; p=0.014). Increasing the county-level density of surgeons improved outcomes, but increasing it beyond 8 CS or 12 GS per 100,000 people did not continue to result in significant reductions in CRC mortality. Similar associations between surgeon density and annual changes in CRC-related death were observed. Counties with a high proportion of Medicare enrollees also showed increased CRC mortality. Conclusions: The presence of CS and GS at the county level is each associated with lower mortality from CRC. However, there appears to be a ceiling effect at which point further increases in their density do not produce continued improvements in CRC outcomes. A balanced strategy of allocating healthcare resources and distributing the surgical workforce evenly across all counties will likely offer the most substantial population-based improvements in CRC mortality. 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 | 0.001 | 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) | 0.002 | 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".