Prevalence and characteristics of adverse drug reactions at admission to hospital: a prospective observational study
Bibliographic record
Abstract
Aims Adverse drug reactions (ADRs) contribute to poorer patient outcomes and additional burden to the healthcare system. However, data on the true burden, relevant types and drugs causing ADRs are lacking. The aim of this study was to determine the prevalence of ADR‐related hospitalization in the general adult population in Singapore and to investigate their characteristics. Methods We prospectively recruited 1000 adult patients with unplanned admission to a large tertiary‐care hospital. Two independent reviewers evaluated all suspected ADRs for causality, type, severity and avoidability. The prevalence of ADR‐related hospitalization was calculated based on ‘definite’ and ‘probable’ ADRs. Logistic regression was used to evaluate predictors for having an ADR at admission. Results The prevalence of all ADRs at admission was 12.4% (95% CI: 10.5–14.6%) and ADRs causing admission was 8.1% (95% CI: 6.5–10.0%). The most common ADRs were gastrointestinal‐related. The most common drug category causing ADRs were cardiovascular drugs. Patients with ADRs had a longer length of stay than those who did not (median 4 vs . 3 days, P = 1.70 × 10 −3 ). About 30% of ADRs at admission were caused by at least one drug with a clinical annotation in the Pharmacogenomics KnowledgeBase (PharmGKB), suggesting that some of these ADRs may have been predicted by pharmacogenetic testing. Conclusions We have quantified the burden and characteristics of clinically impactful ADRs in the Singaporean general adult population. Our results will provide vital information for efforts in reducing ADRs through targeted vigilance, patient education and pharmacogenomics in Singapore.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".