A Method to Calculate Adherence to Inhaled Therapy that Reflects the Changes in Clinical Features of Asthma
Bibliographic record
Abstract
RATIONALE: Currently, studies on adherence to inhaled medications report average adherence over time. This measure does not account for variations in the interval between doses, nor for errors in inhaler use. OBJECTIVES: To investigate whether adherence calculated as a single area under the (concentration-time) curve (AUC) measure, incorporating the interval between doses and inhaler technique, was more reflective of patient outcomes than were current methods of assessing adherence. METHODS: We attached a digital audio device (INhaler Compliance Assessment) to a dry powder inhaler. This recorded when the inhaler was used, and analysis of the audio data indicated if the inhaler had been used correctly. These aspects of inhaler use were combined to calculate adherence over time, as an AUC measure. Over a 3-month period, a cohort of patients with asthma was studied. Adherence to a twice-daily inhaler preventer therapy using this device and clinical measures were assessed. MEASUREMENTS AND MAIN RESULTS: Recordings from 239 patients with severe asthma were analyzed. Average adherence that was based on the dose counter was 84.4%, whereas the ratio of expected to observed accumulated AUC, actual adherence, was 61.8% (P < 0.01). Of all the adherence measures, only adherence calculated as AUC reflected changes in asthma quality of life, β-agonist reliever use, and peak expiratory flow over the 3 months (P < 0.05 compared with other measures of adherence). CONCLUSIONS: Adherence that incorporates the interval between doses and inhaler technique, and calculated as AUC, is more reflective of changes in quality of life and lung function than are the currently used measures of adherence. Clinical trial registered with www.clinicaltrials.gov (NCT 01529697).
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".