Impact of short evidence summaries in discharge letters on adherence of practitioners to discharge medication. A cluster-randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: International concern about quality of medical care has led to intensive study of interventions to ensure care is consistent with best evidence. Simple, inexpensive, feasible and effective interventions remain limited. OBJECTIVE: We examined the impact of one-sentence evidence summaries appended to consultants' letters to primary care practitioners on adherence of the practitioners to recommendations made by the consultants regarding medication for patients with chronic medical problems. DESIGN: Cluster-randomised trial. SETTING: Secondary/primary care interface (urban district hospital/referral practices). PARTICIPANTS: 178 practices received one or more discharge letters with evidence summaries. The 66 practices in the intervention group provided feedback on 172 letters, and the 56 practices in the control group provided feedback on 96 letters. RESULTS: Appending an evidence summary to discharge letters resulted in a decrease in non-adherence to discharge medication from 29.6% to 18.5% (difference adjusted for underlying medical condition 12.5%; p = 0.039). Among the five possible reasons for discontinuing discharge medication, the evidence summaries seemed to have the largest impact on budget-related reasons for discontinuation (2.6% in the intervention versus 10.7% in the control group (p = 0.052)). Most clinicians (72%) were enthusiastic about continuing receiving evidence summaries with discharge letters in routine care. CONCLUSIONS: The one-sentence evidence summary is a simple, inexpensive, well-accepted intervention that may improve primary care practitioners' adherence to evidence-based consultant recommendations.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".