Impact of county-level surgical specialist density on breast (BrCa) and lung cancer (LuCa) mortality.
Bibliographic record
Abstract
6064 Background: Variations in distribution of the surgical workforce may result in differential access to cancer screening and treatment. Our aim was to explore the relationship between county-level surgical specialist density and BrCa and LuCa mortality. Methods: Using data from Area Resource File, US Census and National Cancer Institute, regression models that controlled for cancer incidence, county demographics and socioeconomic factors were constructed to examine the association among a) general surgeon (GS) and radiation oncologist (RO) density with BrCa mortality and b) thoracic surgeon (ThS) and RO density with LuCa mortality. Plastic (PS) and transplant surgeons (TrS) were used as surgical controls as they were not expected to correlate significantly with BrCa or LuCa mortality. Results: A total of 1,557 and 2,044 US counties were analyzed for BrCa and LuCa, respectively: mean incidences were 119 and 75 and death rates were 25 and 59 per 100,000 people, respectively, for BrCa and LuCa. Mean specialist densities were 7.72 (GS), 0.80 (RO), and 0.97 (PS) [for BrCa counties] and 0.55 (ThS), 0.55 (RO), and 0.01 (TrS) [for LuCa counties] per 100,000. When compared to counties with no surgical specialist, those with at least one GS and RO for BrCa and at least one ThS and RO for LuCa were associated with decreased mortality (Table). Increasing the density of GS and RO beyond 9 and 1 per 100,000 did not result in significant reductions in BrCa mortality. Likewise, increasing the density of ThS and RO to above 1 each per 100,000 failed to yield further improvements in LuCa mortality. Counties with more elderly residents also correlated with worse BrCa and LuCa outcomes. Conclusions: The presence of specific surgical specialists is associated with lower BrCa and LuCa mortality. There appears to be a threshold at which point further increase in their density do not contribute to continued improvements in outcomes. Distributing the surgical workforce across all counties will offer population-based improvements in BrCa and LuCa mortality. [Table: see text]
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".