Adherence and influencing factors of high-flow nasal cannula humidified oxygen therapy in elderly patients with stable chronic obstructive pulmonary disease: A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to systematically evaluate and perform a meta-analysis on the adherence to high-flow nasal cannula (HFNC) humidified oxygen therapy and its influencing factors in elderly patients with stable chronic obstructive pulmonary disease (COPD). METHODS: Relevant literature on HFNC and COPD was retrieved from PubMed, EMbase, Web of Science, and Cochrane Library databases. Cross-sectional studies, case-control studies, and cohort studies were included. Screening and quality assessment were conducted using Endnote X9 software. Quality scores were assigned using the Newcastle-Ottawa Scale and the AHRQ assessment tool. Basic information, sample size, and adherence-related factors were extracted, and heterogeneity and publication bias were assessed. RESULTS: A total of 321 articles were initially identified, with 8 English articles involving 325 patients included after screening. Quality assessment yielded five high-quality articles (score > 8), two medium-quality articles (score = 7), and one low-quality article (score = 6). Meta-analysis results showed a COPD patient HFNC adherence rate of 32.7%. Negative factors included the number of acute exacerbations (odds ratio [OR] =2.17), adverse reactions (OR = 4.13), regular follow-up (OR = 9.45), educational level (OR = 5.38), and concurrent medications (OR = 4.71). Positive factors included age < 70 years (OR = 0.45), duration of use (OR = 0.30), inhalation technique (OR = 0.31), treatment satisfaction (OR = 0.35), and adverse reactions (OR = 0.15). Funnel plot and Egger's test results indicated minimal publication bias. CONCLUSION: Adherence to HFNC in elderly COPD patients is relatively low, influenced by negative factors such as the number of acute exacerbations, adverse reactions, regular follow-up, educational level, and concurrent medications. Positive factors include age < 70 years, duration of use, inhalation technique, treatment satisfaction, and adverse reactions.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.059 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".