A discrete choice experiment evaluation of patients' preferences for different risk, benefit, and delivery attributes of insulin therapy for diabetes management
Bibliographic record
Abstract
OBJECTIVE: To evaluate patients' preferences for various attributes of insulin treatment, including route of insulin delivery. METHODS: We used a discrete choice experiment (DCE) to quantify patients' preferences. The attributes (and levels) included in the DCE questionnaire were: glucose control, frequency of hypoglycemic events, weight gain, route of administration for the long-acting and the short-acting insulin, and out-of-pocket cost. Data were analyzed using conditional logit regression and segmented models were also developed to evaluate differences in preferences between subgroups. RESULTS: Two hundred and seventy-four questionnaires were completed. The mean age (SD) of participants was 56.7 (12.9) years. Forty-nine percent of participants were insulin users, and 17% had type 1 diabetes. Overall, patients' ideal insulin treatment would provide better glucose control, result in fewer adverse reactions, have the lowest cost, and be administered orally. Overall, there was a strong positive preference for better glucose control relative to the other attributes. Segmented analyses by insulin use and type of diabetes suggest that there may be an important psychosocial barrier to initiating insulin therapy but that patients tend to adjust to subcutaneous administration once they initiate therapy. CONCLUSIONS: This study illustrates the importance that patients with diabetes place on glucose control and how preferences for insulin therapy differ between subgroups. Specifically, efforts need to be made to overcome the psychosocial barriers to initiating insulin therapy which may lead to improved control through improved treatment acceptance and ultimately improve patients' quality of life and reduce the economic burden of the disease.
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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 | 0.010 | 0.020 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".