P.068 Real world experience with Fingolimod in Canada
Bibliographic record
Abstract
Background: The Gilenya® Go ProgramTM offers education and support services, including coordination of first dose observation (FDO) and follow-up contact to reinforce monitoring recommendations and compliance in fingolimod-treated relapsing-remitting multiple sclerosis (RRMS) patients. Methods: Data were analyzed for patients enrolled in the Canadian Gilenya® Go ProgramTM from March 2011 to January 2016. The retention to fingolimod therapy, reasons for treatment discontinuation and incidence of adverse events (AEs) during treatment are reported. Results: At data cut-off, 3956 patients had completed FDO; 3201 patients were being actively treated. Mean age at enrolment was 41.0 years; 74.9% patients were female. The overall fingolimod exposure was 7869 patient-years. Most recent previous therapies (n=3746) included interferons (43.3%) and glatiramer acetate (29.6%). Most common reasons for switching to fingolimod (n=3674) was lack of efficacy (31.8%). Retention to therapy at data cut-off was 81.3%. AEs (45.2%) were the most common reason (n=334) for treatment discontinuation and included low lymphocyte count/abnormal hematology values (13.8%), gastrointestinal disturbances (6.9%), and elevated liver enzyme levels (7.8%). Adherence to recommended ophthalmic examination was 92.4%. Conclusions: In real-world clinical practice in Canada, adherence to both fingolimod treatment and monitoring was high. The Gilenya® Go Program™ helps to meet the safety monitoring recommendations for fingolimod-treated RRMS 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".