Short-term laboratory and related safety outcomes for the multiple sclerosis oral disease-modifying therapies: an observational study
Bibliographic record
Abstract
Real-world safety data for the oral multiple sclerosis (MS) disease-modifying therapies (DMTs), dimethyl fumarate (DMF), fingolimod, and teriflunomide are important. We examined laboratory test abnormalities and adverse health conditions in new users. Linked laboratory and administrative health data were accessed for all persons with MS (PwMS) filling their first oral DMT prescription in two Canadian provinces. PwMS were followed from first prescription fill until discontinuation, death, emigration or study end. Proportions of PwMS, and incidence rates (IR)/100 person-years, were calculated for ≥1 event of elevated alanine aminotransferase (ALT) (>the upper limit of normal [ULN]; all DMTs), liver toxicity (ALT>3xULN; fingolimod); lymphopenia and proteinuria (DMF), and cardiac arrhythmia, hypertension and pneumonia (all DMTs). Overall, 1,140 PwMS were followed for up to 2 years. De novo elevated alanine aminotransferase affected 13.2% (DMF), 12.4% (teriflunomide), and 30.0% (fingolimod) of users. Liver toxicity affected 2.8% of fingolimod, lymphopenia 3.1% of DMF, and proteinuria 2.9% of DMF users. The incidences of cardiac arrhythmia, pneumonia and hypertension ranged from <1 to 1.86/100 person-years depending on the DMT. The short-term, real-world incidences of abnormal laboratory results or adverse events were consistent with the pivotal clinical trial findings. Longer-term safety data are still needed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".