A randomised trial to evaluate the self-administered standardised chronic respiratory questionnaire
Bibliographic record
Abstract
The original chronic respiratory questionnaire (CRQ), one of the most widely used measures of health-related quality of life (HRQL) in chronic respiratory disease (CRD), is traditionally interviewer administered (IA) and includes an individualised dyspnoea domain. The present authors studied the impact of self-administered (SA) and standardised dyspnoea questions on CRQ measurement properties. In a factorial design multicentre trial, 177 patients with CRD (mean age 67.7 yrs; mean forced expiratory volume in one second per cent predicted 44.6%) were randomised to CRQ-IA (n = 86) or CRQ-SA (n = 91), and to initially complete the standardised or individualised items before and after respiratory rehabilitation. While maintaining validity, the CRQ-SA proved more responsive to changes in HRQL than the CRQ-IA in all domains. Compared with the standardised dyspnoea domain, the individualised dyspnoea domain indicated greater responsiveness. The correlations of baseline scores and change scores with other HRQL instruments indicated good validity of the CRQ-SA. In conclusion, self-administration and standardisation of the chronic respiratory questionnaire maintains validity and responsiveness relative to the interviewer-administered chronic respiratory questionnaire. These results challenge the assumption that interviewer-administered questionnaires are superior to self-administered questionnaires in older patients with chronic respiratory disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".