Discrete choice experiments and conjoint analyses in health screening programs for type 2 diabetes and liver disease: a scoping review
Bibliographic record
Abstract
Background: We aimed to summarize the evidence on the use of discrete choice experiments (DCEs) and conjoint analyses to quantify stakeholders' preferences for screening programs for type 2 diabetes (T2D) and liver diseases, with a specific focus on metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: For this scoping review, five databases (MEDLINE [PubMed], PubMed Central, EMBASE [Ovid], Europe PMC, Google Scholar) were searched with the assistance of a librarian, and deduplicated records were screened by two independent reviewers. Inclusion criteria: using DCE/CA, addressing screening programs for T2D and liver disease, published in English, French, or Spanish after January 1990. Results: Among 2,282 studies, 9 (7 from high- and 2 from low-income countries) elicited preferences for screening for liver disease (n = 1), hepatitis C (n = 1), hepatitis B (n = 1), hepatocellular carcinoma (n = 2), noncommunicable diseases (n = 2), diabetic retinopathy (n = 1), and cardiovascular diseases (n = 1). No studies addressed MASLD screening in T2D. Stakeholders included patients (n = 3), health care providers (n = 1), patients plus health care providers (n = 1), and the general population (n = 3). Studies used 18 structure, 6 process, and 4 outcome attributes. Screening sensitivity, setting, duration, provider, and cost were the most important structure attributes in participant choices. Physician support for treatment was the preferred process attribute. Outcome attributes were the least used, but of major importance (screening adherence followed by treatment) when considered. Conclusions: With no study focusing on MASLD screening in T2D, our scoping review highlights the need to develop a DCE addressing this topic to better design a patient-centred continuum of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".