Effectiveness of adherence to home non-invasive ventilation (NIV) upon hospitalisation rate and mortality in COPD patients with chronic hypercapnic respiratory failure: A structured literature review.
Bibliographic record
Abstract
Introduction: COPD with Chronic Hypercapnic Respiratory Failure (CHRF) has a significant impact on hospitalisation rate and mortality. NIV is a clinically established home-based intervention; however, its impact on hospitalisation rate and mortality remains unclear. Review Aim: This review aims to investigate existing literature to establish the impact of adherence to home NIV on reducing hospitalisation rate and mortality among COPD patients with CHRF. Method: A systematic literature search was conducted in MEDLINE, Cochrane Library, CINAHL, and Embase from May to June 2024. Studies were screened based on predefined inclusion criteria, focusing on home NIV adherence and patient outcomes. The Newcastle-Ottawa Quality Assessment Tool and the National Heart, Lung, and Blood Institute (NIH) quality assessment tool were used. Results: Six observational studies, involving 10,206 participants, assessed the impact of adherence to home NIV on hospitalisation rate and mortality. Five studies were considered high quality and one was moderate quality. Adherence to home NIV for at least four hours per day was associated with a significant reduction in hospitalisation rate and mortality. Conclusion: Adherence to home NIV significantly reduces hospitalisation rate and mortality in COPD patients with CHRF. These findings recommend the adoption of home NIV as a standard care practice for this population and emphasise the importance of patient compliance to maximise the therapeutic benefits of NIV. This review recommends that future research conducts additional RCTs to reinforce its initial findings.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".