Predictors of 2-Year Mortality in Patients Receiving Long-Term Oxygen Therapy: A Prospective Observational Study
Bibliographic record
Abstract
Background: Patients receiving long-term oxygen therapy are in a state of progressive respiratory dysfunction and have high mortality. However, the predictors of mortality in these patients have not yet been established. Objectives: This prospective observational study aimed to identify the predictors of two-year mortality in patients receiving long-term oxygen therapy. Design, Setting/Subjects: This two-year prospective observational study included 96 patients who received long-term oxygen therapy in the outpatient department of the National Hospital Organization Nishiniigata Chuo Hospital in Japan. Measurements: The updated Charlson Comorbidity Index, body mass index, handgrip strength, modified British Medical Research Council scale (mMRC), Barthel Index (BI), and Montreal Cognitive Assessment (MoCA) were collected in 2019 as a baseline. Outcome was defined as mortality due to chronic respiratory disease during the two-year follow-up period, and predictors were estimated using age- and sex-adjusted Cox proportional hazards model. Results: The 83 patients that were followed up, 30 (36%) died. The Cox proportional hazards model estimated handgrip strength (adjusted hazard ratio [HR]: 0.89; 95% confidence interval [CI]: 0.84–0.94; p < 0.01; Wald: 14.38.), mMRC (adjusted HR: 1.96; 95% CI: 1.36–2.83; p < 0.01; Wald: 13.16.), BI (adjusted HR: 0.95; 95% CI: 0.93–0.98; p < 0.01; Wald: 17.07.), and MoCA (adjusted HR: 2.17; 95% CI: 1.31–3.59; p < 0.01; Wald: 9.06) as predictors. Conclusions: This study indicated that handgrip strength, dyspnea, activities of daily living, and cognitive function were predictors of two-year mortality in patients receiving long-term oxygen therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".