Every Cough Matters: Six Months of Monitoring Reveal New Insights into Air Pollution's Impact on Interstitial Lung Disease's Cough.
Bibliographic record
Abstract
Introduction: Cough is a key symptom of interstitial lung disease (ILD), impacting disease progression. While air pollution triggers respiratory symptoms, its role in ILD-related coughing is unknown. This study uses wearable devices to monitor how air pollution affects ILD's cough. Methods: This is a single, prospective cohort study involving ILD patients. Cough was recorded over six months using the ADAMM-RSM™ wearable device. PM2.5, ozone, and wind speed levels were collected from air monitoring stations. Cough and pollutant spikes were defined using established thresholds, and temporal associations were analyzed. Results: Six patients were enrolled, yielding 24,371 cough episodes over 1,170 monitoring days. PM2.5 levels showed the strongest association with increased cough frequency 7 to 14 days post-exposure, peaking on day eight (OR: 2.045; 95% CI: 1.393-2.695). Ozone spikes started an acute increase in coughing within the first 5 days, followed by a gradual return to baseline by day ten. Elevated wind speeds were associated with a reduction in cough. erj;66/suppl_69/PA4015/F1 F1 F1 Discussion: This six-month study highlights the impact of air pollution on cough in patients with ILD. Wearable devices offer valuable insights into environmental triggers, suggesting that air quality assessments should be considered into clinical protocols. Further multicenter studies are needed to validate these initial findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".