Reporting of fatal adverse drug reactions.
Bibliographic record
Abstract
OBJECTIVES: To review the reports of fatal adverse drug reactions (ADRs) submitted to the Ontario Medical Association Adverse Drug Reactions Monitoring Program between 1990 and 1994; to identify drugs associated with fatal outcomes; and to assess the causative role of the drug in these events and the completeness of the data in these reports. METHODS: Drug(s) identified on each ADR report as being responsible for the reaction were considered. Agents were classified by the Anatomical Therapeutic and Chemical classification system. The causality of each ADR report was evaluated by using an algorithmic rating scale. RESULTS: From the Ontario Medical Association database, 97 cases of ADRs that resulted in death were reviewed. One hundred fourteen medications were implicated as "suspect" drugs in the 97 deaths. The most commonly implicated drug classes were musculoskeletal agents, blood and blood-forming organ agents, and nervous system agents. Patients over 65 years of age comprised 60% of this series. After independent assessment as to causality, 13% of the cases were rated as probable, 86% were rated as possible and 1% were rated as doubtful. Seventy per cent of reports did not include information regarding medical history. Forty-two per cent of cases failed to provide adequate information to evaluate the feasibility of the time to onset of the ADR. The use of concomitant drugs was not reported in 12% of cases. CONCLUSIONS: The drugs most frequently implicated in fatal ADRs were consistent with those reported in other studies. Algorithmic causality assessments were of limited value in these reports. The completeness of the reports and adequacy of the information were poor. The type of reporting forms and information provided were not homogenous. There is a need to improve quality of reporting and harmonize reporting forms between monitoring bodies. The feasibility of unique data collection forms and obligatory reporting for fatal ADRs should be considered.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".