COPD: Konsequente Schulung älterer Patienten im Umgang mit ihrem Applikator etablieren
Bibliographic record
Abstract
Background: Maintaining correct inhaler technique is crucial in the management of chronic obstructive pulmonary disease (COPD). We aimed to investigate the inhaler technique in patients with COPD, to compare it immediately after and at 1 month after training, and to identify the predictors of incorrect inhaler use at 1 month after training. Methods: This prospective study was conducted at the COPD clinic of Siriraj Hospital (Bangkok, Thailand). Patients demonstrating improper inhaler use were trained face-to-face by pharmacists. Inhaler technique was re-assessed immediately after and at 1 month after training. The Montreal Cognitive Assessment (MoCA) score, pulmonary function tests, 6-min walk distance (6 MWD), modified Medical Research Council scale score, and COPD Assessment Test (CAT) score were evaluated. Results: Sixty-six patients with COPD who demonstrated at least one critical error during the use of any controller inhaler were enrolled. The mean age was 73.0 ± 9.0 years, and 75.8% patients had moderate/severe COPD. Immediately after training, all patients used dry powder inhalers correctly and 88.1% used pressurized metered-dose inhalers correctly. At 1 month, the number of patients demonstrating the correct technique decreased across all devices. Multivariable analysis revealed that MoCA score ≤ 16 was independently associated with a critical error at 1 month after training (adjusted odds ratio: 12.7, 95% confidence interval: 1.8-88.2, p = 0.010). At 1 month, CAT score (11.4 ± 8.9 vs. 8.4 ± 5.5, p = 0.018) and 6 MWD (351 ± 93 m vs. 372 ± 92 m, p = 0.009) had significantly improved in patients demonstrating the correct technique, and CAT score met the minimal clinically important difference.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".