Evaluating adverse drug event reporting in administrative data from emergency departments: a validation study
Bibliographic record
Abstract
BACKGROUND: Adverse drug events are a frequent cause of emergency department presentations. Administrative data could be used to identify patients presenting with adverse drug events for post-market surveillance, and to conduct research in patient safety and in drug safety and effectiveness. However, such data sources have not been evaluated for their completeness with regard to adverse drug event reporting. Our objective was to determine the proportion of adverse drug events to outpatient medications diagnosed at the point-of-care in emergency departments that were documented in administrative data. METHODS: We linked the records of patients enrolled in a prospective observational cohort study on adverse drug events conducted in two Canadian tertiary care emergency departments to their administrative data. We compared the number of adverse drug events diagnosed and recorded at the point-of-care in the prospective study with the number of adverse drug events recorded in the administrative data. RESULTS: Among 1574 emergency department visits, 221 were identified as adverse drug event-related in the prospective database. We found 15 adverse drug events documented in administrative records with ICD-10 codes clearly indicating an adverse drug event, indicating a sensitivity of 6.8% (95% CI 4.0-11.2%) of this code set. When the ICD-10 code categories were broadened to include codes indicating a very likely, likely or possible adverse event to a medication, 62 of 221 events were identifiable in administrative data, corresponding to a sensitivity of 28.1% (95% CI 22.3-34.6%). CONCLUSIONS: Adverse drug events to outpatient medications were underreported in emergency department administrative data compared to the number of adverse drug events diagnosed and recorded at the point-of-care.
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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 | 0.121 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".