Can disease-specific questionnaires describe the effect of comorbidities on health-related quality of life in patients with COPD? A comparison of disease-specific and generic questionnaires in the COSYCONET cohort
Bibliographic record
Abstract
Background and aim: Health-related quality of life (HRQL) assessment in COPD is important as an individual descriptive measure as well as an endpoint in clinical studies. This study compares HRQL assessment with the generic EQ-5D-3L and two disease-specific questionnaires (SGRQ-C and CAT) in a comprehensive spectrum of COPD disease grades with particular attention on comorbidities. Methods: Using data from 2,291 subjects participating in the German COPD cohort COSYCONET, mean HRQL scores in different GOLD grades were compared by linear regression models adjusting for low (≤3) vs. high (>3) number of comorbidities or a list of 33 self-reported comorbid conditions and age, sex, education, smoking status, BMI. Discriminative abilities of HRQL instruments were assessed by standardized mean differences. Results: All HRQL instruments considered were able to discriminate between COPD grades, with some limitations for the EQ-5D utility score in mild disease. The effect of high comorbidity on HRQL was reflected by both generic and disease-specific questionnaires. The EQ-5D utility put the highest weight on comorbidity while the SGRQ was less influenced by comorbidity but discriminated best between GOLD grades. Psychiatric disorders and peripheral artery disease showed the strongest negative associations with HRQL in all questionnaires. Conclusion: COPD-specific HRQL questionnaires can also reflect the negative effects of comorbid conditions on HRQL but to a smaller degree than generic instruments. Findings may support clinical assessment and choice of HRQL instrument in future studies.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".