A JOURNAL OF NEUROLOGY Cancer risk in multiple sclerosis: findings from
Bibliographic record
Abstract
Findings regarding cancer risk in people with multiple sclerosis have been inconsistent and few studies have explored the possibility of diagnostic neglect. The influence of a relapsing-onset versus primary progressive course on cancer risk is unknown. We examined cancer risk and tumour size at diagnosis in a cohort of patients with multiple sclerosis compared to the general population and we explored the influence of disease course. Clinical data of patients with multiple sclerosis residing in British Columbia, Canada who visited a British Columbia multiple sclerosis clinic from 1980 to 2004 were linked to provincial cancer registry, vital statistics and health registration data. Patients were followed for incident cancers between onset of multiple sclerosis, and the earlier of emigration, death or study end (31 December 2007). Cancer incidence was compared with that in the age-, sex- and calendar year-matched population of British Columbia. Tumour size at diagnosis of breast, prostate, colorectal and lung cancers were compared with population controls, matched for cancer site, sex, age and calendar year at cancer diagnosis, using the stratified Wilcoxon test. There were 6820 patients included, with 110 666 person-years of follow-up. The standardized incidence ratio for all cancers was 0.86 (95 % confidence interval: 0.78–0.94). Colorectal cancer risk was also significantly reduced (standardized incidence ratio: 0.56; 95 % confidence interval: 0.37–0.81). Risk reductions were similar by sex and for relapsing-onset and primary progressive multiple sclerosis. Tumour size was larger than expected in the cohort (P = 0.04). Overall cancer risk was lower in patients with multiple sclerosis than in the age-, sex- and calendar year matched general population. The larger tumour sizes at cancer diagnosis suggested diagnostic neglect; this could have major
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".