Comprehensive care programmes in chronic obstructive pulmonary disease: a systematic review and meta-analysis of randomized controlled trials and real-world studies
Bibliographic record
Abstract
Abstract Background Different comprehensive care programmes (CCPs) have been developed for patients with chronic obstructive pulmonary disorder (COPD), but data regarding their effectiveness have been controversial. PubMed and Embase were searched to 1 st June 2017 for articles that investigated the effects of the different types of CCPs on hospitalization or mortality rates in COPD. Results A total of 67 studies including 3472633 patients (mean age: 76.1±12.7 years old; 41% male) were analyzed. CCPs reduced all-cause hospitalizations (hazard ratio [HR]: 0.70, 95% confidence interval [CI]: 0.63-0.79; P<0.001; I 2 :96%) and mortality (HR: 0.69, 95% CI: 0.573-0.83; P<0.001; I 2 :75%). Subgroup analyses for different CCP types were performed. Hospitalizations were reduced by pharmacist-led medication reviews (HR: 0.54; 95% CI: 0.37-0.78; P=0.001; I 2 :49%), structured care programmes (HR: 0.76; 95% CI: 0.66-0.87; P<0.0001; I 2 :88%) and self-management programmes (HR: 0.79; 95% CI: 0.64-0.99; P<0.05; I 2 :78%), but not continuity of care programmes (HR: 0.70; 95% CI: 0.36-1.36; P=0.29; I 2 :100%), early support discharge or home care packages (HR: 0.97; 95% CI: 0.91-1.04; P=0.37; I 2 :0%) or telemonitoring (HR: 0.61; 95% CI: 0.32-1.18; P=0.14; I 2 :94%). Mortality was reduced by early support discharge or home care packages (HR: 0.49; 95% CI: 0.30-0.80; P<0.01; I 2 :72%), structured care programmes (HR: 0.69; 95% CI: 0.53-0.90; P<0.01; I 2 :61%) and telemonitoring (HR: 0.52; 95% CI: 0.31-0.89; P<0.05; I 2 :0%), but not self-management programmes (HR: 0.79; 95% CI: 0.64-0.99; P<0.05; I 2 :78%). Conclusions Comprehensive care programmes reduce hospitalization and mortality in COPD patients.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".