Modern Innovative Solutions to Improve Outcomes in Severe Asthma: Protocol for a Mixed Methods Observational Comparison of Clinical Outcomes in MISSION Versus Current Care Delivery
Bibliographic record
Abstract
BACKGROUND: Asthma that is poorly controlled and undertreated can progress to more severe disease that is associated with high levels of unscheduled care that requires high-cost therapy, leading to a significant health economic burden. The identification and appropriate referral to a specialist asthma service is also often delayed by several months or years because of poor recognition and understanding of symptom severity. Current severe asthma services may take several months to provide a comprehensive multidisciplinary assessment, often necessitating multiple hospital visits and costing up to £5000 per patient. OBJECTIVE: This study aims to evaluate whether a new service model could identify poorly controlled and potentially severe asthma much earlier in the patient pathway, and then compare clinical outcomes between this new care model with standard care. METHODS: Modern Innovative Solutions to Improve Outcomes in (MISSION) Severe Asthma is a novel service model developed by asthma specialists from Portsmouth and Southampton severe asthma services. MISSION Severe Asthma identified patients with poorly controlled disease from general practice databases who had not been under secondary outpatient care in the last 12 months or who were not known to secondary care. In 1- or 2-stop assessments, a thorough review of diagnosis, disease phenotype, and control is undertaken, and clinical outcomes collected at baseline. RESULTS: A variety of clinical outcomes will be collected to assess the service model. The results will be reported in February 2020. CONCLUSIONS: This protocol outlines a mixed methods study to assess the impact on disease control, unscheduled health care usage, and quality of life in patients seen in the MISSION clinic compared with a closely matched cohort who declined to attend. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/9585.
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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 | 0.076 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".