Association between beta-interferon exposure and hospital events in multiple sclerosis
Bibliographic record
Abstract
PURPOSE: A systematic evaluation of hospital events can be an important surrogate measure for drug effectiveness or adverse effects. The purpose of this study was to examine the association between beta-interferon use and hospital events in a large cohort of patients with multiple sclerosis (MS). METHODS: Retrospective cohort study comparing beta-interferon exposed and unexposed patients using clinical data from the British Columbia MS (BCMS) database linked with health administrative databases, 1996-2008. For each patient, the primary outcome was the number of hospital events in each month, analyzed by quasi Poisson regression. Beta-interferon exposure was examined two ways: current and cumulative exposure. Secondary outcomes included whether a hospital event occurred in each month for each specific primary diagnoses, grouped by International Classification of Diseases categories. RESULTS: Current exposure to beta-interferon was not associated with an altered rate of hospital events (adjusted incident rate ratio 1.018; 95% CI 0.803-1.290). Similarly, there was no association with cumulative exposure. Cumulative beta-interferon exposure was associated with a lower odds of respiratory disease-related hospital events compared to those never exposed to beta-interferon. CONCLUSIONS: Exposure to beta-interferon for MS was not associated with a change in overall hospital event rates. Preliminary evidence suggests that the beta-interferons may have a protective effect against respiratory diseases requiring hospitalization in MS patients.
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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 | 0.004 | 0.014 |
| 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 | 0.000 | 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".