Amid COVID-19: the importance of developing an positive adverse drug reaction (ADR) and medical device incident (MDI) reporting culture for Global Health and public safety
Bibliographic record
Abstract
Abstract Amid COVID - 19 Crisis, reporting adverse drug reactions (ADRs) and medical device incidents (MDIs) to Health Canada or health authorities in every country is crucial for monitoring medication safety and improving public health. Health Canada, for example, through their online database, has facilitated the process of reporting side effects relating to drugs and medical devices. However, several patients and health care professionals still fail to voluntarily report adverse events. For health care providers, some barriers to reporting may include fear of negative feedback, apathy, legal concerns, and uncertainty about whether an incident qualifies as an ADR. In the current COVID-19 Crisis, it is especially important for health care providers to be diligent about reporting Adverse Drug Reactions (ADRs), since misinformation propagated by the media is causing patients to misuse certain medications. We need to shift the current thought process about ADR reporting in order to encourage a positive reporting culture by patients and health care providers.
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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 | 0.167 | 0.280 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".