Association of Neighborhood Socioeconomic Status and Ethnic Diversity with Failure to Rescue in Curative-intent Colorectal Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: To examine the association between neighborhood-level socioeconomic status (SES) and ethnic diversity and failure to rescue (FTR) after curative-intent colorectal cancer (CRC) resection. BACKGROUND OF DATA: FTR is an outcome reflecting a system's ability to detect and treat clinical deterioration after complications. However, little is known about how social characteristics influence FTR in oncologic populations. METHODS: We conducted a population-based retrospective cohort study of adults undergoing resection for stage I-III CRC (2007-2020). Exposures were SES and ethnic diversity defined by ecologic measures from census data. The primary outcome was FTR, defined as in-hospital death following a major postoperative complication. Logistic regression examined the association between exposures and FTR while adjusting for confounders. Subgroup analysis explored associations by cancer site and procedure setting. RESULTS: Among 60,470 patients included, FTR occurred in 1,158 (1.9%). Of those, 25.0% resided in the lowest SES neighborhood (5th quintile) versus 16.9% in the highest (1st quintile) (P<0.001), and 18.5% resided in the most ethnically diverse neighborhoods (5th quintile) versus 21.2% in the least (1st quintile, P=0.12). After adjustment, residing in the lowest SES (Odds Ratio, OR 1.26; 95% confidence interval, CI 1.05-1.52) or most ethnically diverse neighborhoods (OR 1.53, 95%CI 1.26-1.85) was associated with higher odds of FTR compared to patients residing in the highest SES or least ethnically diverse neighborhoods. These observations persisted in the colon but not rectal cancer subgroup and in the emergency but not elective setting. CONCLUSION: These findings outline inequalities in post-operative outcomes by social characteristics pointing towards potential gaps in structures of care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".