Generalizability of Randomised Controlled Trial Eligibility Criteria in Refractory Chronic Cough
Bibliographic record
Abstract
Background: Recruitment into randomized controlled trials(RCT) maybe limited by restrictive eligibility criteria. Refractory Chronic Cough(RCC) trials often require objective and subjective cough criteria. Objectives: Quantify the proportion of RCC patients meeting eligibility criteria based on cough severity visual analogue scale(VAS)(≥40mm on 0-100mm) or 24-hr cough frequency (0-<10,≥10-<20,>20 cough/hr)pre-and post-treatment Methods: Prospective observational single-center cohort study with 24-hr cough frequency, VAS assessment pre-post treatment following ERS cough guidelines. Results: 62 patients were recruited:mean(SD) age 57.7(13.8), 64% female, cough duration 9.9(9.8) yrs, VAS 62.0mm(22.5), LCQ 10.7(3.0). At baseline, 51(82%) met VAS≥40mm; post-treatment, 31(50%)(Fig1A). ≥20 coughs/hr: 26 patients(42%) at baseline, to 20(32%) post-treatment, and 16patients (26%) at both time points(Fig1B). ≥10-<20 coughs/hr: 15 patients(24%) at baseline, 2 were stable, 9 improved to <10 coughs/hr, 4 worsened to ≥20 coughs/hr. 0-<10 coughs/hr:21 patients pre-treatment(34%) mostly remained low. Patients with>20 coughs/hr were older, predominantly female, had longer cough duration, higher VAS, and more triggers. Conclusion: This study suggest about 50% may be eligible on VAS criteria, but this is reduced with an objective cough frequency cut-off. This data highlights the need to balance inclusion thresholds with generalizability. erj;66/suppl_69/PA459/F1 F1 F1
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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 | 0.642 | 0.825 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".