Home monitoring improves endpoint efficiency in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
The objective of this study was to investigate the reliability, feasibility and analytical impact of home-based measurement of forced vital capacity (FVC) and dyspnoea as clinical endpoints in idiopathic pulmonary fibrosis (IPF).Patients with IPF performed weekly home-based assessment of FVC and dyspnoea using a mobile hand-held spirometer and self-administered dyspnoea questionnaires. Weekly variability in FVC and dyspnoea was estimated, and sample sizes were simulated for a hypothetical 24-week clinical trial using either traditional office-based interval measurement or mobile weekly assessment.In total, 25 patients were enrolled. Mean adherence to weekly assessments over 24 weeks was greater than 90%. Compared with change assessment using baseline and 24-week measurements only, weekly assessment of FVC resulted in enhanced precision and power. For example, a hypothetical 24-week clinical trial with FVC as the primary endpoint would require 951 patients using weekly home spirometry compared with 3840 patients using office spirometry measures at weeks 1 and 24 only. The ability of repeated measures to reduce clinical trial sample size was influenced by the correlation structure of the data.Home monitoring can improve the precision of endpoint assessments, allowing for greater efficiency in clinical trials of therapeutics for IPF.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".