Modern Innovative Solutions in Improving Outcomes in Chronic Obstructive Pulmonary Disease (MISSION COPD): Mixed Methods Evaluation of a Novel Integrated Care Clinic
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the second-leading cause of death in the United Kingdom and accounts for 1.7% of bed days in acute hospitals. An estimated two-third of patients with COPD remain undiagnosed. OBJECTIVE: Modern Innovative Solutions in Improving Outcomes in Chronic Obstructive Pulmonary Disease (MISSION COPD) aimed to proactively identify patients from primary care who were undiagnosed or had uncontrolled COPD and to provide a comprehensive integrated multidisciplinary clinic to address the needs of this complex group for improving diagnosis, personalizing therapy, and empowering patients to self-manage their condition. METHODS: This clinic was led by a respiratory specialist team from Portsmouth Hospitals NHS Trust working with five primary care surgeries in Wessex. A total of 108 patients were reviewed, with 98 patients consenting to provide additional data for research. Diagnoses were changed in 14 patients, and 32 new diagnoses were made. RESULTS: Reductions were seen across all aspects of unscheduled care as compared to the prior 12 months, including in emergency general practitioner visits (3.37-0.79 visits per patient, P<.001), exacerbations (2.64-0.56 per patient, P=.01), out-of-hours calls (0.16-0.05 per patient, P=.42), and hospital admissions (0.49-0.12 per patient, P=.48). Improvements were observed in the quality of life and symptom scores in addition to patient activation and patient-reported confidence levels. CONCLUSIONS: This pilot demonstrates that the MISSION model may be an effective way to provide comprehensive gold-standard care that is valued by patients and to promote integration across sectors.
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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 | 0.040 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".