The predictive value of serum galectin 3 for abdominal aortic calcification in maintenance hemodialysis patients: A prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Heterotopic vascular calcification is a common complication of maintenance hemodialysis (MHD) patients. Galectin 3 (Gal-3) has been reported to be associated with cardiovascular calcification. The current study aims to explore the potential predictive value of serum Gal-3 for severe abdominal aortic calcification (AAC) and AAC progression in MHD patients. METHODS: A prospective cohort who underwent hemodialysis during July 2014 at the Blood Purification Center of Ruijin Hospital were followed up for 3 years. Two AAC assessments were performed: one at baseline and one after the 3-year follow-up period. Serum Gal-3 was detected with quantitative ELISA kits. SPSS 23.0 and MedCalc 11.4.2.0 were used to analyze the data. FINDINGS: One hundred and fifty-two patients were recruited. Approximately 59.9% were male, the median age was 60 (50-67) years. Logistic regression analysis indicated that serum Gal-3 was an independent risk factor for both follow-up severe AAC and AAC progression. Receiver operating characteristic (ROC) curve analysis revealed significant prognostic value of serum Gal-3 for predicting severe AAC and AAC progression within 3 years. DISCUSSION: We found serum Gal-3 is correlated to vascular calcification in ESRD patients. Gal-3 may be a potential biomarker of vascular calcification for MHD patients.
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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 | 0.001 |
| 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 | 0.001 | 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".