Impact of glucose and lipid markers on the correlation of calculated and enzymatic measured low‐density lipoprotein cholesterol in diabetic patients with coronary artery disease
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND AIMS: Low-density lipoprotein cholesterol (LDL-C) is widely estimated by Friedewald equation (FE) and Enzymatic test (ET), which are affected by several factors. The aim of this study was to observe the impact of diabetic lipid and glucose patterns on the correlation between FE LDL-C (F-LDL) and ET LDL-C (E-LDL) in patients with coronary artery disease (CAD). METHODS AND RESULTS: A total of 8155 CAD patients were consecutively enrolled and their lipid profiles were measured. The impacts of triglyceride (TG), glycosylated hemoglobin A1c (HbA1c), and high-density lipoprotein cholesterol (HDL-C) on the correlation of F-LDL and E-LDL were examined. The difference value (DV) between F-LDL and E-LDL was compared using ANOVA test. The CAD patients with DM were elder and had higher body mass index, plasma TG compared with those without DM (P < .05 separately). In the whole population, F-LDL was lower than E-LDL but showed a high correlation with E-LDL (r = .970, P = .000). Moreover, as the TG concentrations increased, the DV increased accordingly but the correlation between F-LDL and E-LDL decreased (P < .01). The similar trend was also found in both DM and non-DM patients comparing with different TG groups. However, in patients with DM, there was no significant difference of DV in different HbA1c groups or HDL-C concentrations (P > .05). CONCLUSION: Although F-LDL might underestimate the value of LDL-C, the correlation between F-LDL and E-LDL was clinically acceptable (r = .97), suggesting the LDL-C values measured by two methods were similarly reliable in CAD patients with or without DM.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it