Canadian Experience with Fingolimod: Adherence to Treatment and Monitoring
Bibliographic record
Abstract
BACKGROUND: The Canadian GILENYA® Go ProgramTM provides education and support to people with relapsing-remitting multiple sclerosis during fingolimod treatment. METHODS: Data were collected and analyzed from the time of the first individual enrolled in March 2011 to March 31, 2014. Individuals were excluded if they withdrew from the program prior to receiving the first dose, or had not completed the first dose observation (FDO) at the time of data cut-off. Reports of adverse effects were validated with a database of adverse events reported to Novartis Pharmaceuticals Canada Inc. RESULTS: A total of 2,399 individuals had completed FDO at the end of the three-year observation period. Mean age was 41.2 years; 75.2% were female. The most recent prior therapies reported were interferon-β agents (50.2%), glatiramer acetate (31.1%), natalizumab (14.2%), no prior therapy (3.3%), and other agent (1.1%). Reasons for switching to fingolimod were lack of efficacy (34.9%), side effects (34.6%), and dissatisfaction with injections/infusion (30.4%). Continuation rates with fingolimod at 12, 24 and 30 months were 80.7%, 76.6% and 76.0%, respectively. The discontinuation rate due to reported lack of efficacy during the three-year period was 1.3%. There was 94.4% adherence to the scheduled ophthalmic examination. CONCLUSIONS: The GILENYA® Go ProgramTM captures data for virtually all fingolimod-treated patients in Canada, enabling the evaluation of fingolimod use in routine practice. Ongoing patient support and reminders to take the medication, in conjunction with physicians' and/or patients' perception of the efficacy and tolerability of fingolimod, resulted in a high rate of continuation during longer-term therapy.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".