Efficacy of emergency department‐based interventions designed to reduce repeat visits and other adverse outcomes for older patients after discharge: A systematic review
Bibliographic record
Abstract
AIM: There is an urgent need for effective geriatric interventions to meet the health service demands of the growing older population. In this paper, we systematically review and update existing literature on interventions within emergency departments (ED) targeted towards reducing ED re-visits, hospitalizations, nursing home admissions and deaths in older patients after initial ED discharge. METHODS: Databases Medline, CINAHL, Embase and Web of Science were searched to identify all articles published up to June 2012 that focused on older adults in the ED, included a comparison group, and reported quantitative results in four primary outcomes: ED re-visits, hospitalizations, nursing home admissions and death after initial ED discharge. RESULTS: Of the 2826 titles screened, just nine studies met our inclusion criteria. The studies varied in their design and outcome measurements such that results could not be combined. Two trends surfaced: (i) more intensive interventions more frequently resulted in reduced adverse outcomes than did simple referral intervention types; and (ii) among the lowest intensity, referral-based interventions, studies that used a validated prediction tool to identify high-risk patients more frequently reported improved outcomes than those that did not use such a tool. CONCLUSION: Of the few studies that met the inclusion criteria, there was a lack of consistency and clarity in study designs and evaluative outcomes. Despite this, more intensive interventions that followed patients beyond a referral and the use of a clinical risk prediction tool appeared to be associated with improved outcomes. The dearth of rigorous evaluations with standardized methodologies precludes further 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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".