A systematic review of the effectiveness of discharge care bundles for patients with COPD
Bibliographic record
Abstract
BACKGROUND: A COPD discharge bundle is a set of evidence-based practices aimed at improving patient outcomes after discharge from acute care settings following an exacerbation. We conducted a systematic review on the effectiveness of COPD discharge bundles and summarised their individual care elements. METHODS: Biomedical electronic databases and clinical trial registries were searched from database inception through April 2016 to identify experimental studies evaluating care bundles offered to patients with COPD at discharge. Random-effects meta-analyses of clinical trials data were conducted for hospital readmissions, mortality, and quality of life (QoL). RESULTS: The review included 14 studies (5 clinical trials, 7 uncontrolled trials, and 2 interrupted time series). A total of 26 distinct elements of care were included in the bundles of individual studies. Evidence from four clinical trials with moderate-to-high risk of bias showed that COPD discharge bundles reduced hospital readmissions (pooled risk ratio (RR): 0.80; 95% CI 0.65 to 0.99). There was insufficient evidence that care bundles influence long-term mortality (RR: 0.74; 95% CI 0.43 to 1.28; four trials) or QoL (mean difference in St. George's Respiratory Questionnaire: 1.84; 95% CI -2.13 to 5.8). CONCLUSIONS: Discharge bundles for patients with COPD led to fewer readmissions but did not significantly improve mortality or QoL. Future studies should employ higher quality research methods, fully report care bundle elements, implementation strategies and intervention fidelity to better evaluate the effectiveness of packaging evidence-based interventions together to improve outcomes of patients with COPD discharged from acute care settings.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".