Cancer Screening in Patients With Multiple Sclerosis
Bibliographic record
Abstract
Whether patients with multiple sclerosis (MS) have a higher risk of cancer than the general population has been studied in the context of incidence, prevalence, and risks associated with disease-modifying therapies (DMT). Population-based studies on cancer incidence and prevalence in patients with MS show mixed results depending on cancer subtypes, but generally demonstrate either less or at most comparable rates compared to the general population.1,2 The cancer types with the highest incidence in the MS cohorts include cervical, breast, and digestive cancers.2 Although the overall risk for developing cancer is not higher than in the general population, survival may be lower among patients with MS.3 Because earlier cancer detection is associated with improved mortality for certain cancers, it is critical to assess whether MS patient care supports early diagnosis. Marrie et al.4 had previously published lower breast and colorectal cancer survival rates in patients with MS compared to controls using data from Manitoba and Ontario, Canada. In a group of 779 women with MS and breast cancer compared with 3,116 women without MS, they found that patients with MS had 28% increased hazard for all-cause mortality. Similarly, in a group of 338 patients with MS and colorectal cancer and 1,352 patients with colorectal cancer without MS, survival for patients with MS was lower in the early period following cancer diagnosis.5 The cause for this increased mortality remains to be elucidated but possible explanations include a lower rate of cancer screening in the MS population, either because of limitations to access due to MS disability or competing health interests from MS-related symptoms. Alternatively, complications from MS disease may worsen cancer outcomes directly.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".