Concordance and Associated Factors in Diagnostic Criteria for Prediabetes and Diabetes: An Analysis of Fasting Glucose, Postprandial Glucose, and Glycated Hemoglobin
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (T2DM) and prediabetes are rising chronic health conditions globally. Early and accurate identification of these disorders is crucial for effective prevention and management. The objective was to evaluate the concordance and associated factors of prediabetes and diabetes based on fasting glucose (FG), postprandial glucose (PPG), and glycated hemoglobin (HbA1c). Methods: Primary analysis was conducted on patients from a polyclinic located in Lima, Peru. Prevalences were assessed, concordance was evaluated through the kappa index, and multivariable analyses were performed to identify associated factors for each. Results: A total of 624 participants were included. Isolated values of FG, PPG, and HbA1c for prediabetes accounted for 7.1%, 10.6%, and 5% of cases, respectively, while the intersection of all three accounted for 39.7% of the total. For T2DM, isolated values were represented in 14.5%, 23.2%, and 8.7% of cases, respectively, while the intersection of all three accounted for 44.9%. The concordance between FG and PPG was 0.6970 (P < 0.001), between FG and HbA1c was 0.6163 (P < 0.001), and between PPG and HbA1c was 0.6903 (P < 0.001). Significant associations were found with factors such as gender, age, family history of T2DM, alcohol consumption, and hypertension. Conclusions: The results revealed that PPG detected more cases in isolation, followed by FG and HbA1c. Comparison with previous studies showed variations in prevalence, underscoring the importance of considering multiple criteria in diagnosis. J Endocrinol Metab. 2024;14(1):48-58 doi: https://doi.org/10.14740/jem919
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".