Effect of low‐dose warfarin on D‐dimer levels during sickle cell vaso‐occlusive crisis: a brief report
Bibliographic record
Abstract
OBJECTIVE: To evaluate the activation of clotting systems in patients with sickle cell disease (SCD) by measuring the plasma D-dimer level and to determine the effect of low-dose warfarin on D-dimer level during vaso-occlusive crisis. METHODS: Plasma D-dimer level was measured in 65 blood samples of 37 adult patients with SCD who were hospitalized for vaso-occlusive painful crisis. D-dimer level of patients who were on low-dose warfarin was compared with those patients who were not on any anticoagulation treatment. Analysis of variance (anova) was carried out to determine factors significantly associated with low D-dimer level in patients with SCD. The following factors were included in the anova model; warfarin, homozygous hemoglobin S, history of blood transfusion in past 3 months, hydroxyurea, hemoglobin S%, hemoglobin F%, white blood cell counts, hemoglobin level, platelet count, and plasma fibrinogen level. RESULTS: Overall median D-dimer level in 65 samples was 2.7 microg fibrinogen equivalent units (FEU)/mL (0.34-4). Patients who were on low-dose warfarin had a median D-dimer level of 0.81 microg FEU/mL (0.34-1.8) compared with 3.1 microg FEU/mL (0.94-4) in those patients who were not on anticoagulation treatment. Using anova to model D-dimer levels, only warfarin was significantly correlated with low D-dimer levels after controlling for other variables. CONCLUSIONS: Patients with SCD during vaso-occulsive painful crisis have an elevated D-dimer level. Low-dose anticoagulation treatment is associated with a significant reduction in the D-dimer levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".