D‐dimer levels in maintenance hemodialysis patients: High prevalence of positive values also in the group without predisposing diseases
Bibliographic record
Abstract
We aimed to estimate the prevalence of elevated D-dimer levels in all chronic hemodialysis patients and those without additional disease, and to identify factors associated with increased D-dimer. In 167 chronic hemodialysis patients from our center, D-dimer was measured before dialysis. The effects of age, C-reactive protein (CRP), recent acute illness, vascular access, anticoagulation type, dialysis vintage, and chronic diseases, considered to predispose for increased D-dimer levels, were analyzed. The median D-dimer in the whole group was 966 (inter-quartile range [IQR] 524-1947) μg/L and was positive (>500 μg/L) in 75% of cases. D-dimer was positive in 91% of patients with acute illness, 76% of those with predisposing chronic diseases, but was still positive in 52% of patients without additional disease (i.e., acute illness or predisposing chronic diseases) - median D-dimer was 538.5 (IQR 359-966) μg/L. D-dimer was correlated to patients' age, but not dialysis vintage. In univariate analysis, the D-dimer levels were significantly higher in patients with atrial fibrillation, ischemic heart disease, recent acute illness, increased CRP, dialyzed over a catheter, and on citrate anticoagulation. Multivariate logistic regression showed that only age >65 years (odds ratio [OR] 2.93), catheter (OR 4.86), and positive CRP (OR 4.07) were independently associated with positive D-dimer at 500 μg/L cut-off, while the significance of age disappeared at 2000 μg/L cut-off. To conclude, the high prevalence of positive D-dimer values even in hemodialysis patients without additional disease limits the use of D-dimer for exclusion of thromboembolic diseases in hemodialysis 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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".