C‐reactive protein and risk factors for peripheral vascular disease in subjects with Type 2 diabetes mellitus
Bibliographic record
Abstract
AIMS: To compare the importance of different inflammatory markers and traditional risk factors in predicting peripheral vascular disease (PVD) in patients with Type 2 diabetes mellitus. METHODS: A cross-sectional analysis of 30 Type 2 diabetic patients with PVD defined by ankle-brachial index (ABI) < 0.9, and 60 Type 2 diabetic patients without PVD (ABI > 1.0). Overnight blood was drawn and sent for study. RESULTS: Longer diabetic duration (15 +/- 8 vs. 10 +/- 7 years, P = 0.007), higher serum creatinine level (0.11 +/- 0.04 vs. 0.09 +/- 0.03 mmol/l, P = 0.001), higher total cholesterol/high-density lipoprotein-cholesterol (TC/HDL-C) ratio (5.2 +/- 1.6 vs. 4.3 +/- 1.1, P = 0.004) and increased hypertension status (70% vs. 52%, P = 0.014) and cerebral infarction (CI) history (23% vs. 3%, P = 0.009) were noted in Type 2 diabetes with PVD. Those with PVD also showed significantly higher serum levels of C-reactive protein (CRP) (median 0.282 vs. 0.102 mg/dl, P < 0.001) and interleukin (IL)-6 (10.6 +/- 1.81 vs 1.6 +/- 4.6 pg/ml, P = 0.001). Multivariate regression analysis showed that higher serum levels of C-reactive protein (CRP), longer diabetic duration, and use of angiotensin converting enzyme inhibitor (ACEI) were independently associated with PVD in Type 2 diabetes mellitus. CONCLUSIONS: Type 2 diabetic patients with PVD had longer diabetic duration, higher serum creatinine levels, higher TC/HDL-C ratio, higher hypertension and CI history and higher CRP and IL-6 levels. Only serum CRP level, diabetic duration, and use of ACEI were independently associated with PVD in Type 2 diabetes mellitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".