Under-reporting of Adverse Drug Reactions: The Need for an Automated Reporting System
Bibliographic record
Abstract
Although upwards of 32,000 adverse drug reactions are reported to Health Canada annually, this represents only approximately 5% of cases experienced by Canadians every year. This gross display of underreporting not only results in unrepresentative data in regards to adverse drug reactions, but further discredits databases used by healthcare professionals and in turn compromises the health and safety of Canadians. Major causes of underreporting seen in the literature are ignorance, diffidence and lethargy displayed by healthcare professionals. While Health Canada relies on these professionals to voluntarily report adverse drug reactions, the potential exists for an automated reporting system to remove causes of underreporting. Through integrating such a system with current health informatics technologies such as the electronic health record and utilizing existing health system communication technologies, healthcare professionals will be provided with representative data of adverse drug reactions in Canada and in turn be able to better serve their 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 | 0.074 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".