The utility of common predictive models for detecting undiagnosed diabetes and prediabetes defined by HbA1c in a Chinese population
Bibliographic record
Abstract
AIMS: To evaluate the Hong Kong Primary Care Office (HKPCO) strategy against international guidelines from China, UK, US, Canada and Australia for identifying high-risk individuals requiring diagnostic testing for diabetes mellitus (DM) and pre-DM. METHODS: A cross-sectional study involved adults aged ≥25 without prior DM diagnoses. Participants underwent HbA1c point-of-care testing, with self-reported risk factors and clinical measurements documented. DM was defined as HbA1c ≥6.5 %, and pre-DM as 5.7-6.4 %. Sensitivity, specificity, number needed to test (NNT), and net benefit of different strategies were assessed. RESULTS: Amongst 1810 individuals (age: 59.3 ± 9.2 years, 39.6 % men), 4.8 % had DM and 53.6 % had pre-DM. The sensitivity, specificity and NNT for various detection strategies ranged from 19.5 % to 100 %, 0.58 % to 92.8 % and 8.3 to 20.7 respectively. Over 95 % fulfilled the HKPCO guideline (sensitivity = 100 %, specificity = 0.58 %, NNT = 20.7) without missing DM cases. NICE criteria (sensitivity = 96.6 %, specificity = 19.2 %, NNT = 17.6) correctly reclassified 18.6 % of high-risk participants, providing greater net benefits compared to HKPCO guideline. CONCLUSIONS: The community-based program detected high proportion of Chinese adults at high-risk for undiagnosed DM and pre-DM. Utilizing strategies developed by NICE, or other international strategies, can enhance net benefits and potentially improve cost-effectiveness when compared to the current HKPCO guideline.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".