D‐dimer as a predictor of early neurologic deterioration in cryptogenic stroke with active cancer
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The occurrence of stroke in cancer patients is caused by conventional vascular risk factors and cancer-specific mechanisms. However, cryptogenic stroke in patients with cancer was considered to be more related to cancer-specific hypercoagulability. In this study, we investigated the potential of the D-dimer level to serve as a predictor of early neurologic deterioration (END) in cryptogenic stroke patients with active cancer. METHODS: We recruited 109 cryptogenic stroke patients with active cancer within 72 h of symptom onset. We defined END as an increase of ≥1 point in the motor National Institutes of Health Stroke Scale (NIHSS) score or ≥2 points in the total NIHSS score within 72 h of admission. After adjusting for potential confounding factors in the multivariate analysis, we calculated the odds ratios (ORs) and confidence intervals (CIs) of D-dimer in the prediction of END. RESULTS: Among 109 patients, END events were identified in 34 (31%) patients within 72 h. END was significantly associated with systemic metastasis, multiple vascular territory lesions on the initial magnetic resonance imaging (MRI), initial NIHSS score and D-dimer levels. In the multivariate analysis, the D-dimer level (adjusted OR, 1.11; 95% CI, 1.04-1.17; P < 0.01) and initial NIHSS score (adjusted OR, 1.08; 95% CI, 1.01-1.15; P = 0.03) predicted END after adjusting for potential confounding factors. In the subgroup analysis of 72 follow-up MRIs, D-dimer level was also correlated with new territory lesions on the follow-up MRI in a dose-dependent manner. CONCLUSION: Ischemic stroke patients with active cancer and elevated D-dimer levels appear to be at increased risk for END recurrent thromboembolic stroke.
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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 | 0.004 |
| 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".