Impact of Transitional Care Services for Chronically Ill Older Patients: A Systematic Evidence Review
Bibliographic record
Abstract
Transitions in care from hospital to primary care for older patients with chronic diseases (CD) are complex and lead to increased mortality and service use. In response to these challenges, transitional care (TC) interventions are being widely implemented. They encompass education on self-management, discharge planning, structured follow-up and coordination among the different healthcare professionals. We conducted a systematic review to determine the effectiveness of interventions targeting transitions from hospital to the primary care setting for chronically ill older patients.. Randomized controlled trials were identified through Medline, CINHAL, PsycInfo, EMBASE (1995-2015). Two independent reviewers performed the study selection, data extraction and assessment of study quality (Cochrane "Risk of Bias"). Risk differences (RD) and number needed to treat (NNT) or mean differences (MD) were calculated using a random-effects model. From 10,234 references, 92 studies were included. Compared to usual care, significantly better outcomes were observed: a lower mortality at 3 (RD: -0.02 [-0.05, 0.00]; NNT: 50), 6, 12 and 18 months post-discharge, a lower rate of ED visits at 3 months (RD: -0.08 [-0.15, -0.01]; NNT: 13), a lower rate of readmissions at 3 (RD: -0.08 [-0.14, -0.03]; NNT: 7), 6, 12 and 18 months and a lower mean of readmission days at 3 (MD: -1.33; [-2.15, -0.52]), 6, 12 and 18 months. No significant differences were observed in quality of life. In conclusion, TC improves transitions for older patients and should be included in the reorganization of healthcare services.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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".