Health and Drug Alerts : CMAJ, Health Canada changing way adverse drug information delivered
Bibliographic record
Abstract
The Canadian Adverse Drug Reaction Newsletter (CADRN) is published 4 times a year by Health Canada to alert health professionals to potential medication interactions, adverse drug reactions and concerns about therapeutic products. After the current issue of CADRN appears in this issue of CMAJ, it will be redesigned with a fresh, new look and printed separately from CMAJ, although it will still be delivered with the journal. This will make it easier for physicians to save this important document for future reference. CADRN will still be listed in CMAJ's index and table of contents. In addition, CMAJ's “Health and Drug Alerts” page, which is written by our editorial staff, will regularly (and briefly) explain what is new, important and should be done about serious or under-recognized adverse drug reactions. As well, physicians will be reminded regularly how to report adverse reactions to Health Canada. Drug alerts from Health Canada and the US Food and Drug Administration will continue to be featured regularly in eCMAJ (www.cma.ca/cmaj). We anticipate that these changes will help us deliver concise and relevant information to physicians, and ultimately will increase the reporting of postmarketing adverse events.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.142 | 0.041 |
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".