Action Plan to enhance self-management and early detection of exacerbations in COPD patients; a multicenter RCT
Bibliographic record
Abstract
BACKGROUND: Early detection of exacerbations by COPD patients initiating prompt interventions has shown to be clinically relevant. Until now, research failed to identify the effectiveness of a written individualized Action Plan (AP) to achieve this. METHODS/DESIGN: The current multicenter, single-blind RCT with a follow-up period of 6 months, evaluates the hypothesis that individualized AP's reduce exacerbation recovery time. Patients are included from regular respiratory nurse clinics and allocated to either usual care or the AP intervention. The AP provides individualized treatment prescriptions (pharmaceutical and non-pharmaceutical) related to a color coded symptom status (reinforcement at 1 and 4 months). Although usually not possible in self-management trials, we ensured blinding of patients, using a modified informed consent procedure in which patients give consent to postponed information. Exacerbations in both study arms are defined using the Anthonisen symptom diary-card algorithm. The Clinical COPD Questionnaire (CCQ) is assessed every 3-days. CCQ-recovery time of an exacerbation is the primary study outcome. Additionally, healthcare utilization, anxiety, depression, treatment delay, and self-efficacy are assessed at baseline and 6 months. We aim at including 245 COPD patients from 7 hospitals and 5 general practices to capture the a-priori sample size of at least 73 exacerbations per study arm. DISCUSSION: This RCT identifies if an AP is an effective component of self-management in patients with COPD and clearly differentiates from existing studies in its design, outcome measures and generalizability of the results considering that the study is carried out in multiple sites including general practices.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".