Tolerability and discontinuation rates in teriflunomide-treated patients
Bibliographic record
Abstract
OBJECTIVE: To assess the real-world tolerability of teriflunomide in multiple sclerosis (MS) patients from a large Canadian MS Centre of Care to determine whether previously treated (PT) patients have different tolerability thresholds than treatment-naive (TN) patients, leading to differing discontinuation rates. METHODS: This non-interventional, single-center, retrospective chart review examined all patients who were prescribed commercial teriflunomide between July 2014 and May 2015 at the MS Clinic in the Ottawa General Hospital and Research Institute, Ottawa, Canada. RESULTS: A total of 119 patient charts were reviewed (29 TN and 90 PT). Overall, 19 (15.9%) patients discontinued teriflunomide after a mean treatment duration of 35 weeks. The most common reason for discontinuation was side effects in 8 patients (42%).Discontinuation due to intolerability alone occurred in 13 patients. The number of discontinuations was not sufficient to demonstrate a statistically significant difference between TN and PT patients (p=0.1). CONCLUSION: This retrospective chart review provides some evidence about the real-world tolerability of teriflunomide. Discontinuations were low overall and consistent with previously reported clinical trial data. There was no significant difference in discontinuation rates between patients in the TN and PT groups. We believe that teriflunomide is a safe and well-tolerated oral alternative to injectable therapies.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".