Current and Best Practices of Genetic Testing for Maturity Onset Diabetes of the Young: Views of Professional Experts
Bibliographic record
Abstract
AIMS: Currently, many patients with maturity onset diabetes of the young (MODY) are undiagnosed or misdiagnosed with type 1 or 2 diabetes. This study aims to assess professional experts' views on factors which may influence the current practice of genetic testing for MODY and to explore next steps toward best practice. METHODS: Twelve semistructured interviews were conducted with professional experts. These experts included physicians with potential or actual experience with genetic testing for MODY, representatives of (para)medical professional associations and a staff member of a diabetes patients' organization. RESULTS: Participants differed in their valuation of genetic testing for MODY. While most considered the test useful, not all were convinced of its clinical utility. Other factors mentioned to influence current practice were: (perceived lack of) possibilities for treatment and prevention, patients' perspectives and perceived barriers, such as costs and a lack of knowledge and awareness. Participants agreed that guidelines would be helpful to facilitate expedient testing. CONCLUSIONS: This study identified next steps that should be taken to improve genetic diagnosis and care for patients with MODY. Besides the development of a consensus guideline, other suggestions included more education of healthcare professionals, a clearer allocation of responsibilities with regard to genetic testing for MODY and further research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".