Nasal versus oronasal masks for home non-invasive ventilation in patients with chronic hypercapnia: a systematic review and individual participant data meta-analysis
Bibliographic record
Abstract
Background The optimal interface for the delivery of home non-invasive ventilation (NIV) to treat chronic respiratory failure has not yet been determined. The aim of this individual participant data (IPD) meta-analysis was to compare the effect of nasal and oronasal masks on treatment efficacy and adherence in patients with COPD and obesity hypoventilation syndrome (OHS). Methods We searched Medline and Cochrane Central Register of Controlled Trials for prospective randomised controlled trials (RCTs) of at least 1 month’s duration, published between January 1994 and April 2019, that assessed NIV efficacy in patients with OHS and COPD. The main outcomes were diurnal PaCO2, PaO2 and NIV adherence (PROSPERO CRD42019132398). Findings Of 1576 articles identified, 34 RCTs met the inclusion criteria and IPD were obtained for 18. Ten RCTs were excluded because only one type of mask was used, or mask data were missing. Data from 8 RCTs, including 290 IPD, underwent meta-analysis. Oronasal masks were used in 86% of cases. There were no differences between oronasal and nasal masks for PaCO2 (0.61 mm Hg (95% CI −2.15 to 3.38); p=0.68), PaO2 (−0.00 mm Hg (95% CI −4.59 to 4.58); p=1) or NIV adherence (0·29 hour/day (95% CI −0.74 to 1.32); p=0.58). There was no interaction between the underlying pathology and the effect of mask type on any outcome. Interpretation Oronasal masks are the most used interface for the delivery of home NIV in patients with OHS and COPD; however, there is no difference in the efficacy or tolerance of oronasal or nasal masks.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| 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".