Models of care across the continuum of exacerbations for patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
Exacerbations of chronic obstructive pulmonary disease (COPD) are associated with significant morbidity and mortality, and treatments require a multidisciplinary approach to address patient needs. This review considers different models of care across the continuum of exacerbations (1) chronic care and self-management interventions with the action plan, (2) domiciliary care for severe exacerbation and the impact on readmission prevention and (3) the discharge care bundle for management beyond the acute exacerbation episode. Self-management strategies include written action plans and coaching with patient and family support. Self-management interventions facilitate the delivery of good care, can reduce exacerbations associated with admission, be cost-effective and improve quality of life. Hospitalization as a complication of exacerbation is not always unavoidable. Domiciliary care has been proposed as a solution to replace part, and perhaps even all, of the patient's in-hospital stay, and to reduce hospital bed days, readmission rates and costs; low-risk patients can be identified using risk stratification tools. A COPD discharge bundle is another potentially important approach that can be considered to improve the management of COPD exacerbations complicated by hospital admission; it comprised treatments that have demonstrated efficacy, such as smoking cessation, personalized pharmacotherapy and non-pharmacotherapy such as pulmonary rehabilitation. COPD bundles may also improve the transition of care from the hospital to the community following exacerbation and may reduce readmission rates. Future models of care should be personalized - providing patient education aiming at behaviour changes, identifying and treating co-morbidities, and including outcomes that measure quality of care rather than focusing only on readmission quantity within 30 days.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".