Consistent, Concise and Meaningful: Clinician Perceptions of a Novel Dyspnea Assessment Tool
Bibliographic record
Abstract
BackgroundDyspnea is a prevalent and distressing symptom in interstitial lung diseases with significant effects on patients' quality of life and associated with poorer prognosis. Guidelines recommend a multidimensional dyspnea assessment tool. We developed a validated 9-item scale, the Edmonton Dyspnea Inventory (EDI), in which dyspnea severity is rated across different settings including at rest, during activities of daily living, and self-reported exercise and crises. The standardized, multidimensional tool captures dyspnea intensity for specific contexts, which clinicians can use to manage dyspnea more individually and effectively. Early studies support the feasibility to use the EDI in outpatient settings. The purpose of this study was to explore perceptions of the EDI by community health care professionals.MethodsWe conducted a qualitative study using an inductive approach and open coding for content analysis. Email invitations were sent to community health care professionals and informed consent obtained from the twelve participants. Two focus groups and one key informant interview were conducted. Themes were extracted from transcripts and field note analyses.ResultsFour main themes described their dyspnea assessment with the EDI: the EDI is a meaningful clinical assessment tool; they explicitly engage and educate patients to effectively use the EDI; they use the EDI to personalize and evaluate dyspnea management; and the EDI is valuable for communication and interprofessional collaboration.ConclusionCommunity health care professionals perceived the EDI as valuable to assess dyspnea and personalize management. They recommended it be used in clinical practice and healthcare education for interprofessional dyspnea management for ILD patients.
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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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".