Impaired glucose metabolism in regular occupational health checkups for a military population: surrounding the metabolic enemy
Bibliographic record
Abstract
Introduction: Impaired glucose metabolism, including diabetes and pre-diabetes, is a major cardiovascular risk factor. The aim of this study was to evaluate the glucose metabolism status of employees based on regular occupational health checkups in a military population to plan a more effective program. Methods: From a registry of regular occupational health checkups covering the years 2011 through 2015 in a military medical organization, the study extracted data on age, gender, weight, height, body mass index (BMI), job (medical or non-medical), smoking, history and/or family history of diabetes and hypertension, systolic and diastolic blood pressures, fasting blood glucose (FBS), total cholesterol, triglyceride, and low-density and high-density lipoproteins. Results: Data were collected for 783 apparently healthy individuals, 536 (68.5%) male and 247 (31.5%) female. According to duplicated FBS tests, 17 cases (2.3%) were at diabetic level (FBS≥126 mg/dL), 100 (13.7%) had pre-diabetes (100≤FBS≤125 mg/dL), and 612 (78.2%) had normal FBS (<100 mg/dL). Overall, 1.3% of cases had undiagnosed diabetes and 12.8% had undiagnosed pre-diabetes. Gender, age, BMI, systolic and diastolic blood pressures, and levels of serum triglyceride, total cholesterol, and low-density lipoprotein were significantly associated with impaired glucose metabolism. Non-medical staff had significantly higher prevalence abnormal FBS than medical employees. Importantly, the probability of impaired glucose metabolism increased with clustering of the risk factors. Discussion: A considerable proportion of apparently healthy middle-aged employees of a military medical organization had disturbed glucose metabolism, which was first diagnosed in regular occupational health checkups. A personalized multidimensional approach would enhance individualized risk-assessment models.
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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 | 0.002 |
| 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".