Beta‐interferon exposure and onset of secondary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Beta-interferons (IFNβ) are the most widely prescribed drugs for patients with multiple sclerosis (MS). However, whether or not treatment with IFNβ can delay secondary progressive MS (SPMS) onset remains unknown. Our aim was to examine the association between IFNβ exposure and SPMS onset in patients with relapsing-remitting MS (RRMS). METHODS: A retrospective cohort study using British Columbia (Canada) population-based clinical and health administrative data (1985-2008) was conducted. RRMS patients treated with IFNβ (n = 794) were compared with untreated contemporary (n = 933) and historical (n = 837) controls. Cohort entry was the first clinic visit during which patients became eligible for IFNβ treatment (baseline). The outcome was time from baseline to SPMS onset. Cox regression models with IFNβ as a time-dependent exposure were adjusted for sex, and baseline age, disease duration, disability, *socioeconomic status and *comorbidities (*available for the contemporary cohorts only). Additional analyses included propensity score adjustment. RESULTS: The median follow-up for the IFNβ-treated, untreated contemporary and historical controls were 5.7, 3.7 and 7.3 years, and the proportions of patients reaching SPMS were 9.2%, 11.8% and 32.9%, respectively. After adjustment for confounders, IFNβ exposure was not associated with the risk of reaching SPMS when either the contemporary or the historical untreated cohorts were considered (hazard ratio 1.07; 95% confidence interval 0.93-1.48, and hazard ratio 1.04; 95% confidence interval 0.74-1.46, respectively). Further adjustments and the propensity score yielded results consistent with the main analysis. CONCLUSIONS: Amongst patients with RRMS, use of IFNβ was not associated with a delayed onset of SPMS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".