Thrombosis of the great cerebral vein in a hemodialysis patient
Bibliographic record
Abstract
Cerebral venous thrombosis is a rare condition with various clinical presentations which may delay diagnosis. It is frequently associated with severe consequences. We present the first documented case of thrombosis of the great cerebral vein in a hemodialysis patient. A 29-year-old female patient with end-stage renal disease of unknown etiology was admitted to a hospital with altered consciousness and nausea. Severe headache in the right parietal area had started 2 days before. On examination, she was in the poor overall condition, dysartric, with a severe nystagmus. Urgent brain multislice computerized tomography and magnetic resonance imaging revealed thrombosis of the great cerebral vein with hypodense zones in hypothalamus, thalamus and basal ganglia. She was treated with heparin bolus of 25000 IU with a favorable outcome. Detailed examination demonstrated increased lupus anticoagulant (LA) 1 and LA2 and increased LA1/LA2. Control magnetic resonance imaging performed 1 year later revealed multiple vascular lesions within the brain. Acetylsalicylate was introduced in therapy. Thrombosis of the cerebral veins should be suspected in patients with end-stage renal disease, altered neurological status and signs of increased intracranial pressure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".