Assessment of cancer risk with β-interferon treatment for multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: The risk of cancer after exposure to the β-interferons (IFNβs) for multiple sclerosis (MS) has not been established. We assessed whether IFNβ treatment for MS is associated with cancer risk or the risk of specific cancers in a population-based observational study. METHODS: The British Columbia MS database was linked to the provincial Cancer Registry, Vital Statistics death files and Health Registration files. Using a nested case-control design, MS cancer cases were matched with up to 20 randomly selected MS controls at the date of cancer diagnosis by sex, age (± 5 years) and study entry year using incidence density sampling. Associations between treatment exposure and overall or specific (breast, colorectal, lung and prostate) cancers were estimated by conditional logistic regression, adjusted for MS disease duration and age. Tumour size at cancer diagnosis was compared between treated and untreated patients using the stratified Wilcoxon test to explore potential lead time bias. RESULTS: The cohort included 5146 relapsing-onset MS patients and 48,705 person-years of follow-up, during which 227 cancers were diagnosed. Exposure to IFNβ was not significantly different for cases and controls (OR 1.28; 95% CI 0.87 to 1.88). There was a non-significant trend towards an increased risk of IFNβ exposure in the breast cancer cases (OR 1.77; 95% CI 0.92 to 3.42), but no evidence of a dose-response effect. Tumour size was similar between IFNβ treated and untreated cases. CONCLUSIONS: There was no evidence of an increased cancer risk with exposure to IFNβ over a 12-year observation period. However, the trend towards an association between IFNβ and breast cancer should be investigated further.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".