Effects of long-term oxygen therapy on acute exacerbation and hospital burden: the national DISCOVERY study
Bibliographic record
Abstract
BACKGROUND: Long-term oxygen therapy (LTOT) improves survival in patients with chronic severe resting hypoxaemia, but effects on hospitalisation are unknown. This study evaluated the potential impact of starting LTOT on acute exacerbation and hospital burden in patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and pulmonary hypertension (PH). METHODS: Longitudinal analysis of consecutive patients in the population-based Swedish DISCOVERY cohort who started LTOT between 2000 and 2018 with a follow-up duration≥3 months. Total and hospitalised acute exacerbations of the underlying disease, all-cause hospitalisations, and all-cause outpatient visits were annualised and compared between the year before and after LTOT initiation for each disease cohort, and by hypercapnic status in patients with COPD. RESULTS: Patients with COPD (n=10 134) had significant reduction in annualised rates of total and hospitalised acute exacerbations, as well as all-cause hospitalisations, following LTOT initiation, with increment in those with ILD (n=2507) and PH (n=850). All-cause outpatient visits increased across all cohorts following LTOT initiation. Similar findings were observed in patients with hypercapnic and non-hypercapnic COPD. Sensitivity analyses of patients with 12 months of follow-up showed reduced acute exacerbations and all-cause hospitalisations in the ILD and PH cohorts. CONCLUSION: LTOT is associated with reduced rates of both total and hospitalised acute exacerbations and all-cause hospitalisations in patients with COPD, as well as patients with ILD and PH with 12 months of follow-up. There is increased all-cause outpatient visits in all disease groups following LTOT initiation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".