Optimizing nonpharmacological management following an acute exacerbation of chronic obstructive pulmonary disease
Bibliographic record
Abstract
Though the guidelines for the optimal management of chronic obstructive pulmonary disease (COPD) following an acute exacerbation (AE) are well established, issues associated with poor adherence to nonpharmacological interventions such as self-management advice and pulmonary rehabilitation will impact on hospital readmission rates and health care costs. Systems developed for clinically stable patients with COPD may not be sufficient for those who are post-exacerbation. A redesign of the manner in which such interventions are delivered to patients following an AECOPD is necessary. Addressing two or more components of the chronic care model is effective in reducing health care utilization in patients with COPD, with self-management support contributing a key role. By refining self-management support to incorporate the identification and treatment of psychological symptoms and by providing health care professionals adequate time and training to deliver respiratory-specific advice and self-management strategies, adherence to nonpharmacological therapies following an AE may be enhanced. Furthermore, following up patients in their own homes allows for the tailoring of advice and for the delivery of consistent health care messages which may enable knowledge to be retained. By refining the delivery of nonpharmacological therapies following an AECOPD according to components of the chronic care model, adherence may be improved, resulting in better disease management and possibly reducing health care utilization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".