Top 10 priorities for chronic cough: Canadian James Lind Alliance Priority-Setting Partnership (CAN-COUGH)
Bibliographic record
Abstract
Background: Chronic cough significantly impacts individuals' quality of life and poses challenges for healthcare providers due to limited licensed treatments, side effects of available medications and difficulty accessing nonpharmacological interventions. Understanding priorities for research, education and knowledge dissemination from the perspectives of individuals with chronic cough and healthcare providers can guide future efforts. This study aimed to identify these key priorities in Canada. Study design and methods: We conducted a cross-sectional study using the James Lind Alliance (JLA) method to set priorities. Chronic cough was identified as the healthcare problem. Participants rated items related to research, education and knowledge dissemination in an online survey using a seven-point Likert scale. Results of the survey were reviewed by the Canadian Thoracic Society's (CTS) multidisciplinary working group and patient partners who engaged in a face-to-face Priority-Setting Partnership, to establish a "Top 10" priorities list for chronic cough. Results: 74 individuals with chronic cough and 62 healthcare providers completed the scoping survey. The top-rated priorities included enhancing knowledge of existing treatments, developing new treatments and improving diagnostic testing. The final "Top 10" priorities list emphasised the need for developing infrastructure for evaluating new treatments, improving understanding of biological mechanisms and raising awareness among the public and policymakers. Conclusion: This is the first priority-setting project for chronic cough, highlighting key areas to address in research, education and dissemination. The identified priorities will serve as a foundation for future efforts to improve the management and care of individuals with chronic cough.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".