Modern Innovative Solutions in Improving Outcomes in Chronic Obstructive Pulmonary Disease (MISSION COPD): A Comparison of Clinical Outcomes Before and After the MISSION Clinic
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disorder (COPD) affects over 1 million people in the United Kingdom, and 1 person dies from COPD every 20 minutes. The cost to people with COPD and the National Health Service is huge - more than 24 million working days lost a year and the annual expenditure on COPD is £810 million and £930 million a year. OBJECTIVE: We aim to identify patients with COPD who are at risk of exacerbations and hospital admissions as well as those who have not been formally diagnosed, yet remain at risk. METHODS: This mixed-methods study will use both data and interviews from patients and health care professionals. The project Modern Innovative SolutionS in Improving Outcomes iN COPD (MISSION COPD) will hold multidisciplinary carousel style clinics to rapidly assess the patients' COPD and related comorbidities, and enhance patient knowledge and skills for self-management. RESULTS: This study is ongoing. CONCLUSIONS: This research will capture quantitative and qualitative outcomes to accompany a program of quality improvement through delivery of novel care models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".