Listening to the Patient: Holistic Assessment to Reveal and Manage Breathlessness
Bibliographic record
Abstract
BackgroundBreathlessness is a distressing and prevalent symptom in fibrotic interstitial lung disease. Dyspnea management requires systematic assessment including patients' lived experiences; however, most dyspnea tools are point-in-time numerical severity scales. The Edmonton Dyspnea Inventory was developed to assess severity at rest, during activities of daily living and self-reported activities. It enables documentation of crisis dyspnea episodes and triggers clinicians to guide action plans and dyspnea management. This study is part of a larger project to validate the tool. The purpose was to describe patient perceptions of assessment of breathlessness of patient use of the tool.MethodsPatients with fibrotic interstitial lung disease were invited to share their perceptions and experiences of breathlessness and the tool. Focus groups were led on Zoom©, with patient-participants in their homes. Data were analysed with inductive content analysis for development of themes.ResultsThirteen patients participated in 2 focus groups. There were 4 major themes, each with minor themes: physicians need to explicitly ask about breathlessness; the tool conveys breathlessness and disease progression; the tool increases self-awareness of breathlessness and complexity; and the tool helps prevent crises and manage breathlessness. Patient-participants perceived the tool provided the needed language and means to focus and relay their breathlessness to others.ConclusionPatient-participants reported the tool was easy to understand and integrate in daily living. They recommended its use for general and specialized practitioners. Developed to assess breathlessness, the tool may provide a framework to promote patient self-awareness, describe individual progression, and tailor breathlessness self-management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".