Association of Achilles tendon thickness and LDL-cholesterol levels in patients with hypercholesterolemia
Bibliographic record
Abstract
BACKGROUND: Achilles tendons are the most common sites of tendon xanthomas that are commonly caused by disturbance of lipid metabolism. Achilles tendon thickening is the early characteristic of Achilles tendon xanthomas. The relationship between Achilles tendon thickness (ATT) and LDL-C levels, and risk factors of ATT in patients with hypercholesterolemia, have thus far been poorly documented. METHODS: A total of 205 individuals, aged 18-75 years, were enrolled from March 2014 to March 2015. According to the LDL-C levels and the "Chinese Guidelines on Prevention and Treatment of Dyslipidemia in Adults", all subjects were divided into 3 groups: normal group (LDL-C < 3.37 mmol/L, n = 51); borderline LDL-C group (3.37 mmol/L ≤ LDL-C ≤ 4.12 mmol/L, n = 50); and hypercholesterolemia group (LDL ≥ 4.14 mmol/L, n = 104). ATT was measured using a standardized digital radiography method and the results were compared among the 3 groups. The correlation between ATT and serum LDL-C levels was analyzed by Pearson's correlation, and the risk factors of ATT were determined by the logistic regression model. RESULTS: ATT in borderline LDL-C group was 8.24 ± 1.73 mm, markedly higher than 6.05 ± 0.28 mm of normal group (P < 0.05). ATT in hypercholesterolemia group was 9.42 ± 3.63 mm which was significantly higher than that of normal group (P < 0.005) and that of borderline LDL-C group (P < 0.05). There was a positive correlation between the serum LDL-C levels and ATT (r = 0.346, P < 0.001). The serum LDL-C level was a risk factor (OR = 1.871, 95% CI: 1.067-3.280) while the levels of HDL-C (OR = 0.099, 95% CI: 0.017-0.573) and Apo AI (OR = 0.035, 95% CI: 0.003-0.412) were protective factors of ATT. CONCLUSIONS: ATT might serve as a valuable auxiliary diagnostic index for hypercholesterolemia and used for the assessment and management of cardiovascular disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".