Ten years of adverse drug reaction reports for the multiple sclerosis immunomodulatory therapies: a Canadian perspective
Bibliographic record
Abstract
Adverse drug reaction (ADR) reporting is essential in the post-marketing surveillance of drugs, detection of serious adverse reactions, and has been the basis for drug withdrawals. The study aimed to examine ADR reporting patterns to the multiple sclerosis (MS) immunomodulatory drugs (IMD) in Canada. All ADRs reported to the Canadian ADR Monitoring Program (CADRMP) from 1965 to March 2006 (n=193 208) were accessed and ADRs in which an IMD for MS (beta-interferon or glatiramer acetate) was the suspected drug extracted (n=888 reports were dated March/96-March/06). Almost half of all IMD ADRs reports (438/888) were sourced through the patient compared to 14.9% (10 649/71 373) of all ADRs reported to CADRMP over the same period. Of IMD ADR reports, 88.7% (788/888) were directed through the manufacturer compared to 57.7% (41197/71373) of all ADRs. Encouragement to others involved in patient care, such as pharmacists, nurses and physicians might enhance reporting of MS ADRs. Despite the limitations of ADR reporting data, previously unpublished case reports in several understudied MS populations were detailed: paediatrics (<or= 16 years old, n=4), the elderly (>or= 65 years, n=23) and during pregnancy (n=12). In addition, 46 deaths suspected by the reporter as being related to IMD treatment were detailed as well as three possible drug interactions.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".