A Renewed Charter: Key Principles to Improve Patient Care in Severe Asthma
Bibliographic record
Abstract
Asthma is a heterogenous respiratory disease, usually associated with chronic airway inflammation and hyper-responsiveness, which affects an estimated 339 million people worldwide. Severe asthma affects approximately 5-10% of patients with asthma, approximately 17-34 million people globally, more than half of whom have uncontrolled disease. Severe asthma carries a substantial burden of disease, including unpredictable symptoms and potentially life-threatening flare-ups. Furthermore, severe asthma has a substantial burden on health care systems and economies worldwide. In 2018, a group of experts from the clinical community, patient support groups, and professional organisations joined together to develop the Severe Asthma Patient Charter, which set out six principles to define what patients should expect for the management of their severe asthma and what should constitute a basic standard of care. Since the publication of that original Charter in 2018, several important changes have occurred, including an improved understanding of asthma and effective asthma management; several new therapies have become available; and finally, the COVID-19 pandemic has placed a spotlight on respiratory conditions, the workforces that treat them, and the fundamental importance of health care system resilience. With those developments in mind, we, representatives of the academic, clinical, and patient advocacy group communities, have updated the Charter to Improve Patient Care in Severe Asthma with a focus on six principles: (1) I deserve a timely, comprehensive assessment of my asthma and its severity; (2) I deserve a timely, straightforward referral to an appropriate specialist for my asthma when it is not well controlled; (3) I deserve to understand what makes my asthma worse; (4) I deserve access to treatment and care that reduces the impact of asthma on my daily life; (5) I deserve not to be reliant on systemic corticosteroids; (6) I deserve to be involved in decisions about my treatment and care.
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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 | 0.104 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.016 | 0.059 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".