Clinical trial outcome measures in cough
Bibliographic record
Abstract
Effective therapies for the treatment of cough remain a significant unmet need. However, the development of new therapies relies upon validated tools for cough measurement to demonstrate efficacy in clinical trials. Significant progress has been made in the development of new cough outcome measures in recent years. Symptoms are typically captured by patient-reported outcomes and for cough, the cough severity VAS, cough severity diary and the LCQ have been used most often. As coughing is associated with characteristic movement and sound, objective quantification is feasible, and the VitaloJAK system has been used as the primary end-point in all regulatory trials of novel treatments to date. The development of the P2X3 antagonist, gefapixant, for patients with RCC has afforded the first opportunity in the recent past to present data captured with these cough outcome measures to regulatory bodies making decisions about the approval of new treatments. This chapter focuses on cough outcome measures in the context of clinical trials of novel therapies and discusses recent experiences with some of these end-points in the context of approval of new therapies. Cite as: Smith JA, Kum E, Holt K, et al. Clinical trial outcome measures in cough. In: Song W-J, McGarvey L, Cho PSP, et al. Chronic Cough (ERS Monograph). Sheffield, European Respiratory Society, 2025; pp. 94–106 [ https://doi.org/10.1183/2312508X.10027024 ].
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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 | 0.159 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".