Effect of Long-Term Oxygen Therapy on Reducing Rehospitalization of Patients with Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: The aim of this work is to evaluate whether the addition of home oxygen therapy (HOT) would reduce readmission in chronic obstructive pulmonary disease (COPD) patients. METHODS: PubMed, ScopeMed, Cochrane, Scopus, and Google Scholar databases were searched. The search strategy used the following keywords "chronic obstructive pulmonary disease", the intervention "long-term oxygen therapy", and the outcome "readmission" combined with the AND operator. The Newcastle-Ottawa Scale and Jadad Scale were used for assessing the quality of cohort studies and clinical trials, respectively. A random-effects model was employed in this study after calculating the standard errors by 95% confidence intervals. The I2 statistic and Cochran's Q-test were used to measure heterogeneity. To address heterogeneity, subgroup analyses were carried out according to the length of LTOT, which was classified as "over 8 months" and "under 8 months". RESULTS: = 60% for patients with a length of LTOT treatment under and above 8 months, respectively. A sensitivity analysis was conducted by systematically omitting each study, and it showed no influential studies. Egger's test indicated no publication bias (p = 0.64). CONCLUSIONS: Based on our results in this systematic review, long-tern oxygen therapy (LTOT) at home was associated with a significantly lower risk ratio of hospital readmission. However, the sample sizes in the studies necessitate larger RCTs to evaluate the effect of LTOT on readmission 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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".