Evaluation of Drug-related Emergency Department Admissions in a Tertiary Care Hospital
Bibliographic record
Abstract
Aims: To estimate the incidence, severity, preventability, and cost of medication-related problems.To classify patients admitted with medicationrelated illnesses based on causative factors. Study design: Single-center, cross-sectional, observational study. Materials and methods:Patient details were collected within 24 hours of admission into emergency.The causality assessment for adverse drug reaction (ADR) was assessed using the Naranjo scale.The modified Canadian emergency department triage and the acuity scale was used for assessment of severity of the ADR and the likely outcome. Results:We screened approximately 6,050 admissions; of them, 180 (2.97%) were related to medication-related problems.About 59 (32.8%) were classified as definitely preventable, 92 (51.1%) possibly preventable, and remaining 29 (16.1%) were not preventable.A total of 103 (57.2%) were categorized as severe, 76 (42.2%) as moderate, and 1 as mild.Average number of drugs received prior to admission was 1.29 (±1.15), which increased to 6.93 (±2.34) at the time of analysis.The median (range) cost of medications was $11.70 ($0.32-253.6)per patient per day.Conclusion: Considering the high number of patients admitted with severe illness at the time of admission and the prohibitive cost of medication, we can reduce the financial burden, morbidity, and mortality with drug-related admissions with proper and timely intervention.Also, the inappropriate use of complementary and alternative medications (CAM) should be controlled in order to reduce the incidence of medication-related problems.Clinical significance: Nonadherence and ADRs constitute majority of admissions due to medication-related problems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".