<p>Prevalence and identification of type 1 diabetes in Chinese adults with newly diagnosed diabetes</p>
Bibliographic record
Abstract
Aim: This study aimed to estimate the prevalence of latent autoimmune diabetes of adults (LADA) and classic type 1 diabetes mellitus (T1DM) in newly diagnosed adult diabetes in China. Method: This cross-sectional study involved 17,349 newly diagnosed diabetes in adults aged ≥30 years from 46 hospitals within 31 months. Demographic characteristics, clinical features, and medical history were collected by trained researchers. T1DM as a whole was comprised of classic T1DM and LADA. Classic T1DM was identified based on the clinical phenotype of insulin-dependency, and LADA was differentiated from patients with initially an undefined diabetes type with standardized glutamic acid decarboxylase autoantibody testing at the core laboratory. The age and sex distributions from a large national survey of diabetes in China conducted in 2010 were used to standardize the prevalence of classic T1DM and LADA. Results: Among 17,349 adult patients, the prevalence of T1DM was 5.49% (95% CI: 4.90–6.08%) (5.14% [95% CI: 4.36–5.92%] in males and 6.16% [95% CI: 5.30–7.02%] in females), with 65% of these having LADA. The prevalence of classic T1DM decreased with increasing age ( p <0.05), while that of LADA was stable ( p >0.05). The prevalence of T1DM in overweight or obese patients was 3.42% (95% CI: 3.20–3.64%) and 2.42% (95% CI: 1.83–3.01%), respectively, and LADA accounted for 76.5% and 79.2% in these two groups. Conclusion: We draw the conclusion that T1DM, especially LADA, was prevalent in newly diagnosed adult-onset diabetes in China, which highlights the importance of routine islet autoantibodies testing in clinical practice. Keywords: diabetes, autoimmune, metabolism, differentiation
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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) | 0.001 | 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".