Clinical analysis for relationship between fasting blood glucose level and cardiovascular risk factors in the elderly
Bibliographic record
Abstract
Objective To explore the relationship between fasting blood glucose level and ardiovascular risk factors in elderly people. Methods Totally,378 healthy elderly people aged over 60 years were examined in our hospital,including body mass index (BMI),blood pressure,fasting plasma glucose,lipid profiles,uric acid and fibrinogens (FIB). They were divided into three groups based on the criteria for fasting blood glucose level set by American Diabetes Association (ADA): normal fasting blood glucose (NFG)?impaired fasting blood glucose (IFG) and type-2 diabetes (2-DM) and their cardiovascular risk factors were compared and analyzed. Results There were 284 elderly people in NFG,54 in IFG and 40 in 2-DM (newly diagnosed) groups. BMI,diastolic blood pressure (DBP), total cholesterol (TC),triglyceride (TG) and FIB were much higher in 2-DM group than those in NFG group ( P 0.05). Level of high-density lipoprotein cholesterol (HDL-C) was lower in 2-DM group than that in NFG group ( P 0.05. BMI in IFG group was significantly higher than that in NFG group ( P 0.05). DBP,TC,TG and FIB in 2-DM group were significantly higher and HDL-C was significantly lower than those in IFG group,respectively ( P 0.05). Conclusions The results mentioned above clearly show that the ADA criteria for pre-diabetes status (at the stage with abnormal fasting blood glucose) would significantly underestimate cardiovascular risk factors in the elderly.A new diagnosis of pre-diabetes status is needed to identify high-risk individuals and reduce prevalence of cardiovascular diseases in elderly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".