Adherence to home spirometry among patients with IPF: results from the INMARK trial
Bibliographic record
Abstract
Introduction: Frequent home spirometry may offer advantages over intermittent clinic spirometry in patients with IPF by providing a more accurate estimate of lung function and enabling earlier detection of disease progression or acute exacerbations. However, poor adherence may limit feasibility. Aim: To assess adherence to home spirometry in subjects with IPF in the INMARK trial. Methods: Subjects with IPF and FVC ≥80% predicted were randomised to receive nintedanib or placebo for 12 weeks, followed by open-label nintedanib for 40 weeks. Subjects were asked to perform home spirometry at least once a week and ideally daily. Adherence was assessed as the number of weeks that a subject provided ≥1 home spirometry measurement divided by the number of weeks they were followed in the study. Analyses included all subjects who received ≥1 dose of trial medication. Results: Among 346 subjects, the proportion of subjects with 100% adherence decreased over the trial but remained above 50% (Figure). Over 52 weeks, mean adherence was 86% and median adherence was 96%. The mean number of home spirometry measurements was 3.4 per subject per week. Conclusions: In subjects with IPF and preserved FVC, adherence to home spirometry over 52 weeks decreased over time but remained at a level suggesting that home spirometry might have a role in monitoring lung function in patients with IPF in clinical trials and clinical practice.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".