Objective Assessment of Adherence to Inhalers by Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
RATIONALE: Objective adherence to inhaled therapy by patients with chronic obstructive pulmonary disease (COPD) has not been reported. OBJECTIVES: To objectively quantify adherence to preventer Diskus inhaler therapy by patients with COPD with an electronic audio recording device (INCA). METHODS: This was a prospective observational study. On discharge from hospital patients were given a salmeterol/fluticasone inhaler with an INCA device attached. Analysis of this audio quantified the frequency and proficiency of inhaler use. MEASUREMENTS AND MAIN RESULTS: was 1.3 L, and 59% had evidence of mild/moderate cognitive impairment. By combining time of use, interval between doses, and critical technique errors, thus incorporating both intentional and unintentional nonadherence, a measure "actual adherence" was calculated. Mean actual adherence was 22.6% of that expected if the doses were taken correctly and on time. Six percent had an actual adherence greater than 80%. Hierarchical clustering found three equally sized well-separated clusters corresponding to distinct patterns. Cluster 1 (34%) had low inhaler use and high error rates. Cluster 2 (25%) had high inhaler use and high error rates. Cluster 3 (36%) had overall good adherence. Poor lung function and comorbidities were predictive of poor technique, whereas age and cognition with poor lung function distinguished those with poor adherence and frequent errors in technique. CONCLUSIONS: These data may inform clinicians in understanding why a prescribed inhaler is not effective and to devise strategies to promote adherence in COPD.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".