Assessment of Home-based Monitoring in Adults with Chronic Lung Disease: An Official American Thoracic Society Research Statement
Bibliographic record
Abstract
Abstract Background There is increasing interest in the use of home-based monitoring in people with chronic lung diseases to improve access to care, support patient self-management, and facilitate the collection of information for clinical care and research. However, integration of home-based monitoring into clinical and research settings requires careful consideration of test performance and other attributes. There is no published guidance from professional respiratory societies to advance the science of home-based monitoring for chronic lung disease. Methods An international multidisciplinary panel of 32 clinicians, researchers, patients, and caregivers developed a multidimensional framework for the evaluation of home-based monitoring in chronic lung disease developed through consensus using a modified Delphi survey. We also present an example of how the framework could be used to evaluate home-based monitoring using spirometry and pulse oximetry in adults with asthma, bronchiectasis/cystic fibrosis, chronic obstructive pulmonary disease, and interstitial lung disease. Results The PANACEA framework includes seven domains (test Performance, disease mANAgement, Cost, patient Experience, clinician Experience, researcher Experience, and Access) to assess the degree to which home-based monitoring assessments meet the conditions for clinical and research use in chronic lung disease. Knowledge gaps and recommendations for future research of home spirometry and pulse oximetry in asthma, bronchiectasis/cystic fibrosis, chronic obstructive pulmonary disease, and interstitial lung disease were identified. Conclusions The development of the PANACEA framework allows standardized evaluation of home-based monitoring in chronic lung diseases to support clinical application and future research.
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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 | 0.078 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".