An Evaluation of Patients' Willingness to Trade Symptom-Free Days for Asthma-Related Treatment Risks: A Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Not taking treatment preferences into account may lead to patients' inappropriate use of asthma treatments. The objective of this study was to quantify these preferences, in terms of risk-benefits trade-offs, for six asthma treatment attributes using a discrete choice experiment (DCE). METHODS: Adult asthma patients (n = 157) participated in the study. The custom-designed DCE measured preferences for treatment effectiveness (symptom-free days), potential risk (oral thrush and tremor/heart palpitation), ease of use (frequency of daily administration and number of inhalers required), and cost. A nested logit model was used to determine the relative preferences of each attribute, from which the marginal rates of substitution were calculated. Segmented models were used to test for interactions between cost and treatment benefit with socioeconomic status and medication use. RESULTS: Relationships between preferences and all attributes were in the hypothesized direction. On average, patients were willing to pay an additional $14 per month to receive one additional symptom-free day, and $26, $79, and $112 monthly to avoid one, two, and three annual episodes of oral thrush, respectively. Income and the magnitude of short-acting beta -agonist use also affected treatment preferences. CONCLUSIONS: Overall, asthma patients desired treatments that offered more symptom-free days, but they were willing to trade days without symptoms in exchange for a reduction in adverse events and greater convenience.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".