Colorectal Cancer Survival in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We tested the hypothesis that overall and cancer-specific survival after a colorectal cancer diagnosis is lower in persons with multiple sclerosis (MS) than in those without MS using a retrospective matched cohort design. METHODS: Using population-based administrative data in Manitoba and Ontario, we identified persons with MS from a validated case definition and linked these cohorts to cancer registries to identify those with colorectal cancer. We selected persons with colorectal cancer and without MS, matching 4:1 on birth year, sex, cancer diagnosis year, and region. We used Cox proportional hazards regression to compare all-cause survival between cohorts, adjusting for age at cancer diagnosis, cancer diagnosis year, income, region, and Elixhauser comorbidity score. We compared cancer-specific survival between cohorts using a cause-specific hazards model. We pooled findings across provinces using random-effects meta-analysis. Complementary analyses using a subcohort from Ontario, adjusted for cancer stage and disability status, as measured from the use of home care or long-term care services. RESULTS: We included 338 MS cases and 1,352 controls with colorectal cancer. The mean (SD) age at cancer diagnosis was 64.7 (11.1) years. After adjustment, MS was associated with an increased hazard for all-cause death that was highest 6 months after diagnosis (hazard ratio [HR] 1.45, 95% confidence interval [CI] 1.19-1.76) and then declined over time (HR [95% CI] at 1 year 1.34 [1.09-1.63], 2 years 1.24 [0.99-1.56], 5 years 1.10 [0.80-1.50]). MS was associated with increased cancer-specific death at 6 months after diagnosis only (HR 1.29, 95% CI 1.04-1.61). After adjustment for cancer stage, MS was associated with an increased hazard of death due to any cause (1.60, 95% CI 1.16-2.21) and with cancer-specific death (HR 1.47, 95% CI 1.02-2.12). The association of MS and all-cause death was partially attenuated after adjustment for disability status (HR 1.37, 95% CI 0.97-1.92), as was the association with cancer-specific death (HR 1.34, 95% CI 0.91-1.97). DISCUSSION: Overall and cancer-specific survival was lower in persons with than without MS in the early period after colorectal cancer diagnosis. Further study is warranted to determine what factors underlie these worse outcomes.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".