The impact of socioeconomic factors on outcomes of patients with locally advanced rectal cancer (LARC).
Bibliographic record
Abstract
612 Background: Patients with rectal cancer may experience disparities in outcomes due to various socioeconomic (SES) factors. We assessed the impact of SES factors on outcomes in patients with LARC who received neoadjuvant chemoradiation (nCRT) and surgery (Sx) in three Canadian provinces. Methods: Associations between clinical variables, demographics, community characteristics (2015 Canadian Census data), distance and time to the nearest cancer center (mapping software), and outcomes were evaluated. Results: 1,098 patients were included (Table 1). Median follow-up time was 67.8 months. The 5-year survival rate was 0.80 (95% CI 0.77-0.82). Factors predictive of disease-free survival in univariate analysis (UVA) included age, worse performance status (PS), driving time > 1 hour, median community income, and driving distance > 100 km. Factors that remained significant in multivariate analysis (MVA) included age (HR 1.01; 95% CI 1.00-1.02; p = 0.01), worse PS (HR 1.30; 95% CI 1.01-1.68; p = 0.04) and driving time > 1 hour (HR 1.31; 95% CI 1.01-1.71; p = 0.04). Factors predictive of overall survival in UVA included age, worse PS, driving time to the cancer centre > 1 hour, median community income, and community proportion with post-secondary education. Factors that remained significant in MVA included age (HR 1.03; 95% CI 1.02-1.04; p < 0.001), worse PS (HR 1.41; 95% CI 1.03-1.94; p = 0.03), and median community income (HR 1.00; 95% CI 1.00-1.00; p = 0.05). Conclusions: Outcomes of patients with LARC undergoing nCRT are significantly associated with driving time to the nearest cancer centre and community household income. Further efforts to understand and reduce these socioeconomic disparities are warranted. [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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".