Utility scores in patients with oxygen-dependent COPD: A case-control study
Bibliographic record
Abstract
Background : The comparison of health-related quality of life among patients with different chronic health conditions is essential to the prioritization of limited health resources from cost-effectiveness analyses . To do so, utility measures summarize the health-related quality of life of an individual using a single number usually between 0 (death) and 1 (full health) and are useful to quantify the benefits of health care interventions in terms on quality-adjusted life years (QALYs). Objective: To determine utility scores in patients with oxygen-dependent COPD to be used in cost-utility analyses. Methods: 68 patients with oxygen-dependent COPD (the cases) were matched, on a 1:1 basis, to controls according to gender, age (± 5 years) and FEV1 (±5% predicted). Utility scores were obtained from the SF-6D, a measure derived from the SF-36. Results: 68 oxygen-dependent patients (42 men; mean age: 71 years; mean FEV1: 36% predicted) were successfully matched with as many controls. We found a clinically and statistically significant difference in mean utility scores between cases (0.59 ± 0.07) and controls (0.62 ±0.08; p = 0.004). The same differences were observed in men and women. Conclusion : Oxygen-dependence adds to the burden of disease in terms of quality of life. These utility scores can be used in cost-utility analyses involving patients with oxygen-dependent COPD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".