The use of international classification of diseases codes to identify hospital admissions linked with adverse drug events: Validation study
Bibliographic record
Abstract
AIMS: Several methods exist to identify hospital admissions related to adverse drug events (ADEs). Clinical adjudication by healthcare professionals is the gold standard but is labour-intensive. Spontaneous reporting and routinely collected healthcare data using a set of International Classification of Diseases (ICD) codes often underestimate the prevalence of ADE-related admissions. Expanding the set of ICD codes could improve detection; however, validation is limited. The objective was to describe the agreement between ADE-related ICD-10 codes and clinically adjudicated ADE-related admissions in 2 settings. METHODS: This study analysed 2 datasets: 1102 readmissions from a hospital in the Netherlands (180 ADE-related) and 1228 admissions from a hospital in the Czech Republic (195 ADE-related). Clinical adjudication involved expert review including causality assessment to identify ADE-related hospital admissions. The sensitivities and specificities were calculated for a narrow code set (higher drug-likelihood codes containing words like drug-induced) and a broad code set of ICD-10 codes (including codes very likely, likely and possibly ADE-related). RESULTS: The narrow ICD-10 set showed a sensitivity of 3% (95% confidence interval [CI] 2-6%) and a specificity of 99.6% (95% CI 99-100%). The broad set increased sensitivity to 27% (95% CI 23-32%), with specificity decreasing slightly to 92% (95% CI 91-94%). Preventable ADEs were identified less frequently with both ICD-10 code sets. CONCLUSIONS: Only 3% of ADE-related admissions were detected by the narrow ICD-code set and 27% by the broad code set without a significant drop in the specificity. ADE-related ICD codes seem to serve as triggers for 1 in 4 ADE-related hospital admissions.
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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 | 0.037 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".