Towards disease stability in COPD management: patient perspectives
Bibliographic record
Abstract
Objectives: An emerging treatment ambition for COPD is to obtain disease stability, which comprises measures such as lung function optimisation, symptom control and reductions in exacerbations. In order to provide optimal and personalised care, an understanding of patient expectations is crucial; therefore, we explored individual perceptions surrounding COPD disease stability. Methods: A patient advocacy advisory board was held in January 2024 to discuss patient perceptions of COPD management including insights into disease stability and its role as a treatment goal. Results: Advisors (N=6) described stable disease as a state where patients can achieve a “sense of normalcy” and “predictability”, which is likely to be personal to the patient. Advisors agreed that disease stability was an appropriate and ambitious treatment goal for most patients, underpinned by avoiding exacerbations and hospital visits (Figure A). Advisors prioritised quality of life, agreeing that poor disease control negatively affected this, and emphasised patient wellbeing as a key end goal (Figure B). Conclusion: Key measures of disease stability, in consideration of patient aspirations and clinical characteristics, are desirable goals for COPD management. Personalisation of treatment strategies to improve clinical outcomes underpins the achievement and maintenance of disease stability to improve quality of life for patients living with COPD. Funding: GSK erj;64/suppl_68/PA1171/F1 F1 F1
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".