P.051 Patient-reported adverse events on Multiple Sclerosis disease-modifying therapies in an urban tertiary MS clinic
Bibliographic record
Abstract
Background: Disease-modifying therapies (DMT) have been shown to reduce relapses and delay disability in individuals with relapsing-remitting multiple sclerosis (MS). However, these medications can cause adverse events (AE) leading to poor adherence. To better understand their clinical utility, this study examined real-life experiences with DMT in a tertiary MS clinic. Methods: A retrospective chart review (1999-2015) was conducted to evaluate the prevalence of AE and discontinuation rates of Health Canada approved DMT. Results: 445 MS patients who have used at least one DMT in their lifetime were reviewed. Among first-line injectable therapies, interferon beta (IFNβ) 1-α IM users (49.6%) were most likely to report an AE. Flu-like reactions and injection site reactions were the most commonly reported AE. Among first-line oral therapies, BG-12 users (58.5%) were most likely to report an AE. The most common AE were flushing and gastrointestinal upset. DMT that were most frequently discontinued as a result of AE were IFNβ 1-α SC (39.3%), IFNβ 1-α IM (36.8%) and BG-12 (34.6%). Conclusions: The prevalence of AE and discontinuation rate were congruent. In comparison with recent literature, this study demonstrated lower prevalence of AE but equivocal or higher discontinuation rates. This discrepancy could represent a more realistic depiction of the impact that DMT AE have on 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".