D‐dimer as a predictor of early neurologic deterioration in cryptogenic stroke with active cancer
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it