Detection of serious adverse drug reactions using diagnostic codes in the International Statistical Classification of Diseases and Related Health Problems
Bibliographic record
Abstract
Canadian hospitals are legally required to report serious adverse drug reactions (ADRs). This study aimed to assess the ability to detect serious ADRs from diagnostic codes and the potential benefit of adding stand-alone diagnostic codes to the regular process for detecting serious ADRs. In this descriptive study, clinical pharmacists and a reference work on drug-induced diseases allowed to identify diagnostic codes in the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Canada (ICD-10-CA), reflecting clinical manifestations related to an ADR. Records for admissions to a large urban mother-child hospital in the fiscal year 2018-2019, as coded by medical archivists, were analysed. Of 69 ICD-10-CA diagnostic codes reflecting an ADR identified, 38 were included in the detailed analysis of patient records and 18 (which appeared in 130 admissions) deemed to indicate a serious ADR. Among the 130 admissions analysed, 70 serious ADRs were identified, of which 52 were previously detected by the regular process and 18 were not, increasing the detection of serious ADRs by 34.6% (18/52). These 18 serious ADRs were newly identified from 11 of the 18 codes reflecting clinical manifestation of a serious ADR. Adding ICD-10-CA diagnostic codes not associated with external cause codes can increase the capacity to detect serious ADRs in hospitals. Over a 12-month period, the use of 11 such diagnostic codes increased the detection capacity for serious ADRs by 34.6%.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".