Performance of trigger tools in identifying adverse drug events in emergency department patients: a validation study
Bibliographic record
Abstract
AIMS: Trigger tools are retrospective surveillance methods that can be used to identify adverse drug events (ADEs), unintended and harmful effects of medications, in medical records. Trigger tools are used in quality improvement, public health surveillance and research activities. The objective of the study was to evaluate the performance of trigger tools in identifying ADEs. METHODS: This study was a sub-study of a prospective cohort study which enrolled adults presenting to one tertiary care emergency department. Clinical pharmacists evaluated patients for ADEs at the point-of-care. Twelve months after the prospective study's completion, the patients' medical records were reviewed using eight different trigger tools. ADEs identified using each trigger tool were compared with events identified at the point-of-care. The primary outcome was the sensitivity of each trigger tool for ADEs. RESULTS: Among 1151 patients, 152 (13.2%, 95% confidence intervals (CI) 11.4, 15.3%) were diagnosed with one or more ADEs at the point-of-care. The sensitivity of the trigger tools for detecting ADEs ranged from 2.6% (95% CI 0.7, 6.6%) to 15.8% (95% CI 10.6, 22.8%). Their specificity varied from 99.3% (95% CI 98.6, 99.7) to 100% (95% CI 99.6, 100%). CONCLUSION: The trigger tools examined had poor sensitivity for identifying ADEs in emergency department patients, when applied manually and in retrospect. Reliance on these methods to detect ADEs for quality improvement, surveillance, and research activities is likely to underestimate their occurrence, and may lead to biased estimates.
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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 | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".