Nocturnal non-invasive positive pressure ventilation for stable chronic obstructive pulmonary disease
Bibliographic record
Abstract
BACKGROUND: Nocturnal non-invasive positive pressure ventilation (NIPPV) might be beneficial in stable hypercapnic patients with chronic obstructive pulmonary disease (COPD). However, evidence remains equivocal as conflicting results have been published. OBJECTIVES: To determine the effect of nocturnal non-invasive positive pressure ventilation via nasal mask or face mask in patients with COPD. SEARCH STRATEGY: An initial search was carried out using the Cochrane Airways Group COPD RCT register using the search terms: (nasal ventilat* OR positive pressure OR NIPPV). An additional search was done by hand searching of abstracts from meetings of the American Thoracic Society, British Thoracic Society and European Respiratory Society. SELECTION CRITERIA: Randomised controlled trials in stable patients with COPD that compared nocturnal non-invasive positive pressure ventilation plus standard therapy with standard therapy alone. DATA COLLECTION AND ANALYSIS: Data extraction was performed by two independent reviewers. MAIN RESULTS: The only outcome for which the 95% confidence interval excluded zero was PI max. The 95% confidence interval (CI) of the other outcomes included zero. These included FEV1,FVC, PaCO2, sleep efficiency and 6-minute walking distance (6MWD). The mean effect on 6MWD was modest at 27.5 m, but the 95% CI were wide (-28.1, 81.8 m) suggesting that some patients had a big improvement. Such patients could not be identified a priori. REVIEWER'S CONCLUSIONS: Nocturnal NIPPV for at least 3 months in hypercapnic patients with stable COPD had no consistent clinically or statistically significant effect on lung function, gas exchange, respiratory muscle strength, sleep efficiency or exercise tolerance. However, the small sample sizes of these studies precludes a definite conclusion regarding the effects of NIPPV in COPD.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.020 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".