Innovating the treatment of COPD exacerbations: a phone tele-system to increase Action Plan adherence
Bibliographic record
Abstract
Rationale: COPD exacerbations are the first cause of preventable hospital admissions in Canada. Effective self-management with written Action Plans and case management accelerates exacerbation recovery. These strategies could be improved with the use of communication technologies. The objectives of this study were to determine whether a phone tele-system increases Action Plan adherence during pulmonary exacerbations in a COPD clinic. Methods: Forty patients from the COPD clinic at the Montreal Chest Institute were enrolled in an initial study. Patients received regular automated phone calls and could contact the tele-system at any time. The tele-system issued alarms to case managers during exacerbations. Detailed data from patients9 behaviours during exacerbations were recorded monthly by a third party. The tele-system was then implemented at a large scale to cover 290 patients. Healthcare use was assessed with hospital databases. Results: Thirty three patients (12 M/21F; 69±6.9 years) completed the one-year initial study. A total of 93 exacerbations were reported. Fifty three percent of patients initiated Action Plan medication by themselves and 38% contacted their case manager within 72 hrs. Overall, Action Plan adherence was observed during 72% of exacerbations. The number of COPD-related hospitalizations decreased significantly after tele-system enrolment. Conclusions: Patients enrolled in this study demonstrated higher Action Plan adherence rates than what has been previously reported in the literature. The large-scale implementation of the tele-system resulted in a significant reduction of COPD related hospitalizations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".