Disparities in Outcomes following Resection of Locally Advanced Rectal Cancer
Bibliographic record
Abstract
Surgical margins following rectal cancer resection impact oncologic outcomes. We examined the relationship between margin status and race, ethnicity, region of care, and facility type. Patients undergoing resection of a stage II–III locally advanced rectal cancer (LARC) between 2004 and 2018 were identified through the National Cancer Database. Inverse probability of treatment weighting (IPTW) was performed, with margin positivity rate as the outcome of interest, and race/ethnicity and region of care as the predictors of interest. In total, 58,389 patients were included. After IPTW adjustment, non-Hispanic Black (NHB) patients were 12% (p = 0.029) more likely to have margin positivity than non-Hispanic White (NHW) patients. Patients in the northeast were 9% less likely to have margin positivity compared to those in the south. In the west, NHB patients were more likely to have positive margins than NHW patients. Care in academic/research centers was associated with lower likelihood of positive margins compared to community centers. Within academic/research centers, NHB patients were more likely to have positive margins than non-Hispanic Other patients. Our results suggest that disparity in surgical management of LARC in NHB patients exists across regions of the country and facility types. Further research aimed at identifying drivers of this disparity is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".