A randomized-controlled trial of a patient-centred intervention in high-risk discharged older patients
Bibliographic record
Abstract
BACKGROUND: The risk of early reattendance after discharge has been proposed as a performance indicator for emergency departments (EDs), but is not uniform in all patients. Those individuals at the highest risk of reattendance may benefit from an intense intervention to reduce this risk, and our objective was to test this hypothesis in a clinical trial. METHODS: A randomized-controlled trial was conducted in the EDs of two hospitals. Very high-risk adults aged 65 years and older, identified using a validated risk-prediction nomogram and being discharged from ED, were randomized to receive a postdischarge patient-centred intervention or standard care. The intervention focused on identifying and supporting patients to address risk factors for future hospital presentation. The primary outcome measure was any unplanned ED reattendance within 28 days. Secondary outcomes included 28-day and 1-year hospital usage, institutionalization and death. RESULTS: We enrolled 164 patients, 82 in each study arm. There was an 8% absolute (95% confidence interval: -7%-20%) and a 20% relative risk reduction for an intervention patient making an unplanned ED reattendance within 28 days. This difference was not statistically significant (P=0.26). CONCLUSION: This postdischarge intervention was associated with only small and nonsignificant reductions in ED reattendance.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".