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
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".