Characteristics of adverse medication events in a children's hospital
Bibliographic record
Abstract
AIM: To compare adverse medication events (AMEs) reported in children, via the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) coding with events reported via other data sources. METHOD: AME reports were retrieved using codes Y40-Y59 and X40-X44 over 6 months. Patients' charts were manually reviewed to identify events associated with error and/or harm with medicines during a hospital admission. Medication name, group, error, harm and alert documentation were recorded. Clinical incidents and pharmacist interventions were reviewed for the same period. RESULTS: Two hundred sixty-three events from January to June 2011 were recorded by ICD-10 coding in 180 patients. After duplicated, missing or unrelated events were excluded and 146 AMEs remained. In the same period, 117 AMEs were reported as incidents and 190 as pharmacist interventions. In total, 276 children with 447 events were reported via all sources. Little duplication between data sources was evident. In total, 158 events involved harm, with 135 of these from ICD-10 coding, 16 from incident reports and 2 pharmacist interventions (including 6 events from multiple sources). Error was involved in 3% of ICD10 reports, 97% of incidents and 100% of interventions. Only 14% of harm-related events from ICD-10 were documented on the medical record clinical alert. Chemotherapy accounted for 31% of harm-related events, antimicrobials 18%, corticosteroids 14% and narcotics 12%. CONCLUSION: Of the harm-related events, 85% were documented via ICD-10 coding with few documented in other databases. Review of ICD-10-coded AMEs can provide valuable information to improve patient safety and quality.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".