Research priorities in chronic breathlessness: international expert consensus
Bibliographic record
Abstract
Background: While the need for breathlessness research has been identified by health professionals and people caring for or living with chronic breathlessness, which research should be prioritised is unknown. We sought to identify expert views of research priorities for chronic breathlessness. Methods: Authors (n=74) of dyspnea/chronic breathlessness publications identified in Scopus were invited to a three-round Delphi survey. In Round 1, authors were asked to list ‘three to five questions that should be priorities for breathlessness research in the next 5 years’. Items suitable for rating on a 1-9 point Likert scale were devised from Round 1 responses and rated in two further rounds. A priori consensus was defined as ≥70% of respondents rating an item as important (Likert rating 7-9) and interquartile range (IQR) ≤2 (dispersion). Results: 31 respondents (9 countries, 5 professions) completed Round 1 (n=24 consistent in all 3 rounds). At the end of Round 3, 26 items met consensus. The six top research priorities were identifying: 1) alternatives to opioid therapy (consensus=91% [IQR 1.25]); 2) effective breathlessness self-management models for patients and carers (91% [2]); 3) distinct underlying pathological mechanisms across diseases (89% [1]); 4) therapies with an acceptable safety profile and good clinical benefit with a relatively long expected survival (87% [2]); 5) optimal pharmacological management of breathlessness (85% [2]); and 6) effective behavioural approaches (85% [2]). Conclusion: The research priorities generated by this study are a starting point for conversations between patients, carers, clinicians and researchers within the chronic breathlessness community.
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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 | 0.400 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".