Multiple Sclerosis and the Cancer Diagnosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The multiple sclerosis (MS) population's survival from breast cancer and colorectal cancer is compromised. Cancer screening and timely diagnoses affect cancer survival and have not been studied in the MS cancer population. We investigated whether the diagnostic route, cancer stage, or diagnostic interval differed in patients with cancer with and without MS. METHODS: We conducted a matched population-based cross-sectional study of breast cancers (2007-2015) and colorectal cancers (2009-2012) in patients with MS from Ontario, Canada, using administrative data. Exclusion criteria included second or concurrent primary cancers, no health care coverage, and, for the patients without MS, those with any demyelinating disease. We based 1:4 matching of MS to non-MS on birth year, sex (colorectal only), postal code, and cancer diagnosis year (breast only). Cancer outcomes were diagnostic route (screen-detected vs symptomatic), stage (stage I vs all others), and diagnostic interval (time from first presentation to diagnosis). Multivariable regression analyses controlled for age, sex (colorectal only), diagnosis year, income quintile, urban/rural residence, and comorbidity. RESULTS: We included 351 patients with MS and breast cancer, 1,404 matched patients with breast cancer without MS, 54 patients with MS and colorectal cancer, and 216 matched patients with colorectal cancer without MS. MS was associated with fewer screen-detected cancers in breast (odds ratio [OR] 0.68 [95% CI 0.52, 0.88]) and possibly colorectal (0.52 [0.21, 1.28]) cancer. MS was not associated with differences in breast cancer stage at diagnosis (stage I cancer, OR 0.81 [0.64, 1.04]). MS was associated with greater odds of stage I colorectal cancer (OR 2.11 [1.03, 4.30]). The median length of the diagnostic interval did not vary between people with and without MS in either the breast or colorectal cancer cohorts. Controlling for disability status attenuated some findings. DISCUSSION: Breast cancers were less likely to be detected through screening and colorectal cancer more likely to be detected at early stage in people with MS than without MS. MS-related disability may prevent people from getting mammograms and colonoscopies. Understanding the pathways to earlier detection in both cancers is critical to developing and planning interventions to ameliorate outcomes for people with MS and cancer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".