<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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".