Development of the McMaster Cough Severity Questionnaire (MCSQ) for patients with chronic cough
Bibliographic record
Abstract
<bold>Background:</bold> Cough severity is an important endpoint to assess the impact of therapies for refractory or unexplained chronic cough (RCC/UCC). <bold>Objective:</bold> To develop a patient-reported outcome measure addressing cough severity in patients with RCC/UCC. <bold>Methods:</bold> Phase 1: A systematic survey, focus groups, and expert consultation generated 51 items. Phase 2: From a list of 51 items, 100 patients identified those they had experienced in the previous year and rated their importance on a 5-point scale. The draft MCSQ included items reported to occur most frequently and that had the highest importance scores. Patient feedback on the draft MCSQ led to elimination of redundant items. Phase 3: Another 100 patients completed the draft MCSQ, from which we performed an exploratory factor analysis to further refine items on the MCSQ and assess its construct validity and internal consistency. Results: Item reduction using the impact method led to selection of 15 items for the draft MCSQ. Patient feedback on the 15 items led to elimination of 5 redundant items. An exploratory factor analysis of the 10-item MCSQ led to selection of two domains (intensity and frequency), elimination of one item that demonstrated substantial cross-loading, and another that had high inter-item correlations. The final MCSQ consisted of 8 items, each with a one-week recall period, under two domains. The 8-item MCSQ had high internal consistency (α=0.89) and high cross-sectional convergent validity (r=0.76 [95% CI 0.66 to 0.83]) with the 100-mm cough severity visual analogue scale. <bold>Conclusions:</bold> Initial evidence supports the validity of the MCSQ. Further studies should assess its properties in measuring change over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".