Frequency of cough triggers associated with worse quality of life in patients with chronic cough
Bibliographic record
Abstract
Background: Patients with chronic cough (CC) often describe coughing to low levels of thermal, mechanical, and chemical stimulation. The underlying mechanism is thought to be due to cough hypersensitivity syndrome. It is unclear if the number of triggers is related to greater burden on cough quality of life (QoL) and severity. Objective: To evaluate the relationship between cough triggers and quality of life. Methods: This was a prospective observational single center cohort study. Treatments were based on the most recent ERS cough guideline including pharmacological and non-pharmacological treatment options. Cough assessments included measuring Leicester Cough Questionnaire (LCQ), cough severity visual analogue scale (VAS 0-100mm) before and after treatment. Patients were asked if their cough was triggered by a common list of triggers. Total number of triggers were added. Results: We recruited 69 patients with CC (49 females; mean (S.D) age, 55.7±14.5 yrs, median cough duration 7 yrs). Patients reported mean (S.D) total cough triggers 8.88 (4.05). There was in inverse correlation between total cough triggers and LCQ (R= -0.38, p<0.001) (Fig 1A) but not cough severity VAS, r=0.17, p=0.14, Fig 1B. Conclusions: In patients with CC, greater number of cough triggers was associated with lower cough QoL. Whether the number of cough triggers may be used as a predictive tool will be evaluated. erj;64/suppl_68/PA331/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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 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".