A Non-Pharmacological Cough Therapy for People with Interstitial Lung Diseases: A Case Report
Bibliographic record
Abstract
Purpose: To explore the feasibility of a non-pharmacological cough control therapy (CCT) customized for a client with interstitial lung disease (ILD). Client Description: An 83-year-old female with hypersensitivity pneumonitis, and chronic cough for 18 years treated previously with pharmacological treatment for the underlying lung disease and gastroesophageal reflux disease, as well as lozenges and breathing and relaxation strategies. Intervention: Four cough education and self-management sessions (45-60 minutes each) facilitated by a physiotherapist and speech-language pathologist via videoconference were conducted. Session topics included mechanisms of cough in ILD, breathing and larynx role in cough control, trigger identification, cough suppression and control strategies, and psychosocial support towards behaviour change using motivational interviewing. Measures and Outcome: The following assessments were conducted prior to and one week after the intervention: semi-structured interviews, Leicester Cough Questionnaire, King's Brief Interstitial Lung Disease questionnaire, Functional Assessment of Chronic Illness Therapy Fatigue Scale, modified Borg Scale for severity and intensity of cough, and the Global Rating of Change Questionnaire. Implications: Implementing the CCT was feasible. The client reported increased perceived cough control, a reduction in exhaustion from coughing bouts, and a better understanding of the mechanisms behind cough management and suppression. Improvements were also observed in cough-related quality of life, severity, and intensity.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".