Decoding objective cough features in progressive pulmonary fibrosis: A 6-month feasibility study
Bibliographic record
Abstract
OBJECTIVES: Cough is a significant medical problem in progressive pulmonary fibrosis. This study assessed the feasibility of utilizing an objective cough monitoring device over an extended duration and whether big data analytics could correlate with the Leicester Cough (LCQ) and King's Brief Interstitial Lung Disease (K-BILD) questionnaires. METHODS: Patients with progressive pulmonary fibrosis were enrolled and followed throughout the six-month protocol. The primary outcome was for the participants to use the device for more than 70% of the days of the expected study duration. The secondary outcome involved an exploratory big data analysis, correlating the measurements from the device to the questionnaires scores. RESULTS: Eight patients were enrolled in the study. Only one met the primary outcome. Nevertheless, the amount of data recorded during the study allowed for the correlation of cough intensity with the scores of LCQ and K-BILD. Cough count varied over time in all the patients, independently of the questionnaires. CONCLUSION: For the first time, this study suggests that long-term digital cough data obtained directly from the patients through wearables may enable monitoring the course of diseases such as progressive pulmonary fibrosis.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".