The effect of early in‐hospital medication review on health outcomes: a systematic review
Bibliographic record
Abstract
AIMS: Adverse drug events are an important cause of emergency department visits, unplanned admissions and prolonged hospital stays. Our objective was to synthesize the evidence on the effect of early in-hospital pharmacist-led medication review on patient-oriented outcomes based on observed data. METHODS: We systematically searched eight bibliographic reference databases, electronic grey literature, medical journals, conference proceedings, trial registries and bibliographies of relevant papers. We included studies that employed random or quasi-random methods to allocate subjects to pharmacist-led medication review or control. Medication review had to include, at a minimum, obtaining a best possible medication history and reviewing medications for appropriateness and adverse drug events. The intervention had to be initiated within 24 h of emergency department presentation or 72 h of admission. We extracted data in duplicate and pooled outcomes from clinically homogeneous studies of the same design using random effects meta-analysis. RESULTS: We retrieved 4549 titles of which seven were included, reporting the outcomes of 3292 patients. We pooled data from studies of the same design, and found no significant differences in length of hospital admission (weighted mean difference [WMD] -0.04 days, 95% confidence interval [CI] -1.63, 1.55), mortality (odds ratio [OR] 1.09, 95% CI 0.69, 1.72), readmissions (OR 1.15, 95% CI 0.81, 1.63) or emergency department revisits at 3 months (OR 0.60, 95% CI 0.27, 1.32). Two large studies reporting reductions in readmissions could not be included in our pooled estimates due to differences in study design. CONCLUSIONS: Wide confidence intervals suggest that additional research is likely to influence the effect size estimates and clarify the effect of medication review on patient-oriented outcomes. This systematic review failed to identify an effect of pharmacist-led medication review on health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".