A Model Applied to a Real Life Situation: Self-Management with a Written Action Plan for Early Treatment of COPD Exacerbations
Bibliographic record
Abstract
Background: We hypothesized that self-management education with the use of a written action plan provided by a nurse case manager can help patients to gain the proper skills to start an early treatment for an acute exacerbation. Methods: COPD patients from an outpatient clinic with access to a written action plan and self-administered prescription were instructed to initiate their antibiotics and/or prednisone in case of exacerbation, and call their nurse case manager for supervision. The following data was collected: symptoms change, patients delay in taking action to treat their exacerbations (starting antibiotics and prednisone, calling the case manager) and use of hospital services. Results: We report on 187 exacerbations occurring in a cohort of 113 moderate / severe COPD patients with FEV1 of 37 ± 16% predicted (mean ± SD). 161 exacerbations were supervised by the case manager at the time of the event. The remaining 26 exacerbations were detected after the event. 87% of the supervised exacerbations presented with 2 major symptoms (increased dyspnea, increased sputum volume and/or purulent sputum). Patient’s delay to initiate treatment in supervised exacerbations was 2.04 ± 1.8 days; 85% took action to treat the exacerbation within 3 days. The treatment for supervised and unsupervised exacerbations was similar (slightly more antibiotics and prednisone were used for unsupervised ones) and they had similarly favourable outcomes in terms of health services use, with 68.5% of the exacerbations not requiring any hospital services. Conclusions: Patients can take an active role, acquire the skills to recognize exacerbation symptoms and start an early treatment of antibiotics and prednisone according to the directives of their written action plan.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".