Evaluation of a Questionnaire to Assess Compliance with Anti‐asthma Medications
Bibliographic record
Abstract
Compliance with anti-asthma medication is essential in controlling symptoms and exacerbations in patients with asthma. Unfortunately, not all patients adhere to their treatment regimen, and it is difficult for clinicians to estimate a patient's compliance, since there is no simple and accurate method currently available to assist in its assessment. The objective of this study was to assess the validity and accuracy of utilizing clinical information regarding a patient's prescription refill frequency, inhaler emptying rate, reported forgetfulness, and short-acting bronchodilator usage to predict daily, anti-inflammatory intake. A questionnaire based on the clinical information described above was administered verbally to asthma patients with varying disease severities. Patient responses were compared to the patient's own pharmacy records. Questions that correlated significantly with pharmacy records were subsequently fit into a multiple regression model. Out of 147 eligible participants, 70 completed the questionnaire and had comprehensive pharmacy data available. There was a significant correlation between daily anti-inflammatory intake as estimated by pharmacy records and daily anti-inflammatory intake as determined by inhaler emptying rate (p<0.05), reported forgetfulness (p<0.05), and short-acting bronchodilator usage (p<0.05). These items were fit into a multiple regression model, which was predictive of daily anti-inflammatory intake as determined by pharmacy records. The sensitivity and specificity of our regression model in detecting non-compliance was 44% and 86%, respectively. We conclude that by inquiring into a patient's inhaler emptying rate, reported forgetfulness, and short-acting bronchodilator usage, a clinician may be able to more accurately estimate a patient's daily intake of anti-inflammatory medication.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".