Precision treatment of beta-cell monogenic diabetes: a systematic review
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Bibliographic record
Abstract
BACKGROUND: Beta-cell monogenic forms of diabetes have strong support for precision medicine. We systematically analyzed evidence for precision treatments for GCK-related hyperglycemia, HNF1A-, HNF4A- and HNF1B-diabetes, and mitochondrial diabetes (MD) due to m.3243 A > G variant, 6q24-transient neonatal diabetes mellitus (TND) and SLC19A2-diabetes. METHODS: The search of PubMed, MEDLINE, and Embase for individual and group level data for glycemic outcomes using inclusion (English, original articles written after 1992) and exclusion (VUS, multiple diabetes types, absent/aggregated treatment effect measures) criteria. The risk of bias was assessed using NHLBI study-quality assessment tools. Data extracted from Covidence were summarized and presented as descriptive statistics in tables and text. RESULTS: There are 146 studies included, with only six being experimental studies. For GCK-related hyperglycemia, the six studies (35 individuals) assessing therapy discontinuation show no HbA1c deterioration. A randomized trial (18 individuals per group) shows that sulfonylureas (SU) were more effective in HNF1A-diabetes than in type 2 diabetes. Cohort and case studies support SU's effectiveness in lowering HbA1c. Two cross-over trials (each with 15-16 individuals) suggest glinides and GLP-1 receptor agonists might be used in place of SU. Evidence for HNF4A-diabetes is limited. Most reported patients with HNF1B-diabetes (N = 293) and MD (N = 233) are on insulin without treatment studies. Limited data support oral agents after relapse in 6q24-TND and for thiamine improving glycemic control and reducing/eliminating insulin requirement in SLC19A2-diabetes. CONCLUSION: There is limited evidence, and with moderate or serious risk of bias, to guide monogenic diabetes treatment. Further evidence is needed to examine the optimum treatment in monogenic subtypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it