A time-clocked care pathway on severe asthma: A global consensus by Delphi methodology
Bibliographic record
Abstract
Background: Despite guidelines for severe asthma (SA) management, more than half of SA patients worldwide have poor disease control. Patients with uncontrolled disease remain at risk for future exacerbations for prolonged periods while awaiting assessments, appointments with specialists, and initiation of appropriate treatments. Aim: To develop a global consensus on the optimal approach to time-clocked care in the management of SA from primary through to specialist care. Methods: The project used a modified Delphi method, led by a global steering group of clinicians and a global patient group representative. The group developed consensus statements on best practice for each stage of the SA care pathway. These were independently audited and ratified by the group. Approved statements were then surveyed across 5 countries to assess agreement among relevant healthcare practitioners. Consensus was defined a priori as 75% agreement. Results: 63 statements were developed, 53 achieved consensus. Responses were received from 500 healthcare practitioners. Results show very strong agreement that SA care must be timely, with prompt referral for assessments by specialists. 95% agreed that time-clocked care needs to be introduced to reduce wait times for treatment optimization. 83% agreed there should be a target of 18 weeks from referral to the implementation of optimized care, including the initiation of biologics. Conclusions: This work demonstrates what is needed for optimal SA care and provides a framework which can be adapted into local health systems. This framework, if implemented, will help shift SA care towards early intervention by facilitating timely referral, treatment, and follow-up.
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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 | 0.239 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".