Capturing Adult Patient Preferences Toward Benefits and Risks of Second-Line Antihyperglycemic Medications Used in Type 2 Diabetes: A Discrete Choice Experiment
Bibliographic record
Abstract
OBJECTIVES: To estimate the strength of preferences, relative importance and trade-offs that patients with type 2 diabetes make between characteristics of antihyperglycemic medications. METHODS: We conducted a discrete choice experiment with a sample of Canadians with type 2 diabetes. Respondents completed 14 choice tasks and choose between 2 hypothetical drug alternatives, described by 8 characteristics (cost, efficacy, life expectancy, risk of macrovascular event, risk of microvascular event, risk of severe hypoglycemia, risk of minor side effects and risk of rare but serious side effects). An opt-out option was also provided. Characteristics used to describe the 2 drugs were identified using a literature review, focus groups and interviews. A multinomial mixed logit model was used to estimate choice probabilities. Willingness to pay (WTP) was used to assess trade-offs between characteristics. RESULTS: A total of 502 survey responses were included. The average age of participants was 59±12 years. Participants were 59% men, and 62% had diabetes for at least 6 years. All characteristics were found to significantly influence choice. On average, patients were willing to pay a monthly cost for their therapy of $134 to achieve 3 additional years of life; $49 and $36 for a 20% reduction in their risk of macrovascular and microvascular events, respectively; $34 for a 1% drop in glycated hemoglobin; $29 for a 50% less risk of severe hypoglycemia over 10 years; $29 for a 50% less risk of a minor side effect and $17 for a 50% less risk of a rare but serious side effect over 10 years. CONCLUSIONS: All 8 characteristics were shown to significantly influence choice, with cost and life expectancy carrying the most weight and serious and minor side effects carrying the least weight.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".