Long-term oxygen therapy in COPD patients: population-based cohort study on mortality
Bibliographic record
Abstract
Purpose: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide and is associated with a growing and substantial socioeconomic burden. Long-term oxygen therapy (LTOT), recommended by current treatment guidelines for COPD patients with severe chronic hypoxemia, has shown to reduce mortality in this population. The aim of our study was to assess the standardized mortality ratios of incident and prevalent LTOT users and to identify predictors of mortality. Patients and methods: We conducted a 2-year follow-up population-based cohort study comprising all COPD patients receiving LTOT in the canton of Bern, Switzerland. Comparing age- and sex-adjusted standardized mortality ratios, we examined associations between all-cause mortality and patient characteristics at baseline. To avoid immortal time bias, data for incident (receiving LTOT <6 months) and prevalent users were analyzed separately. Results: At baseline, 475 patients (20% incident users, n=93) were receiving LTOT because of COPD (48/100,000 inhabitants). Mortality of incident and prevalent LTOT users was 41% versus 27%, respectively, p <0.007, and standardized mortality ratios were 8.02 (95% CI: 5.64–11.41) versus 5.90 (95% CI: 4.79–7.25), respectively. Type 2 respiratory failure was associated with higher standardized mortality ratios among incident LTOT users (60.57, 95% CI: 11.82–310.45, p =0.038). Conclusion: Two-year mortality rate of COPD patients on incident LTOT was somewhat lower in our study than in older cohorts but remained high compared to the general population, especially in younger patients receiving LTOT <6 months. Type 2 respiratory failure was associated with mortality. Keywords: COPD, long-term oxygen therapy, mortality, type 2 respiratory failure
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".