The impact of socioeconomic status on stage at presentation, receipt of diagnostic imaging, receipt of treatment, and overall survival in colorectal cancer patients.
Bibliographic record
Abstract
10565 Background: Socioeconomic factors have been identified to influence patterns of care in colorectal cancer yet current literature findings are sparse, conflicting, and often incomplete. As such, this study investigates the impact of socioeconomic status (SES) on stage at presentation, receipt of diagnostic imaging, receipt of treatment, and overall survival (OS) in a universal healthcare system. Methods: The Ontario Cancer Registry was accessed to identify a cohort of patients diagnosed with colorectal adenocarcinoma from 2007-2016 in Ontario, Canada. Linkage to administrative datasets allowed study of the impact of SES, measured by mean neighbourhood household income divided into quintiles (Q1-Q5; Q1 = lowest income), on stage, imaging, treatments, and OS. Multivariable regression analyses of all endpoints were adjusted for age, sex, comorbidity, and rurality with OS models also adjusting for imaging and treatment. Results: 39,802 colon and 13,164 rectal patients were identified. Lower SES patients were more likely to present at a higher stage in both cohorts. Lower SES colon patients were less likely to receive magnetic resonance imaging (MRI) of the abdomen, liver resection, adjuvant oxaliplatin, and all palliative systemic therapies studied. In rectal patients, lower SES was associated with decreased receipt of MRI pelvis, rectal cancer resection in early stages, adjuvant oxaliplatin, and most palliative chemotherapies studied. All OS models found that lower SES was associated with poorer OS. Conclusions: These findings suggest disparities across the continuum of cancer care persist even within a universal healthcare system. Further efforts should be directed towards temporal research, identifying barriers, and subsequently applying this information to actionable policies.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".