Bibliographic record
Abstract
In this issue of the Canadian Respiratory Journal, Bourbeau et al (pages 480‐486) publish what could be loosely described as a validation of a French‐Canadian translation of the Chronic Respiratory Questionnaire (CRQ) (1) and the St George′s Respiratory Questionnaire (SGRQ) (2). They translated the questionnaires and went over them in detail until they were convinced that the questionnaires actually asked the questions that they were supposed to. They then administered them to two groups of chronic obstructive pulmonary disease (COPD) patients. One group had stable COPD and was tested twice with a two‐week period between tests to examine test‐retest reproducibility. The second group consisted of patients who either had an exacerbation of their COPD or who underwent rehabilitation for their disease. Both situations are associated with improvements in quality of life that should be detectable by the questionnaires. These results were compared with a third standard quality of life questionnaire. The results were very good. The questionnaire results met expectations: they were reproducible in stable patients and showed when patients improved. In psychometric terms, they were reliable and valid. I recommend the paper to people who are interested in developing and testing such instruments, both for the knowledge displayed by the authors and for the clarity of their presentation.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.070 |
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".