Imaging of the Intracranial Venous System
Bibliographic record
Abstract
BACKGROUND: Evaluation of the intracranial venous system has historically been performed with conventional catheter-based digital subtraction angiography (DSA). The continued importance of DSA can not be overstated in light of its inherent option of endovascular intervention and thrombolysis for cerebral venous thrombosis. DSA is, however, an invasive procedure with associated risks, including radiation exposure, and adverse effects of iodinated contrast medium. DSA also suffers from the limitations of 2-dimensional planar imaging. For these reasons, noninvasive imaging techniques are playing a greater role in evaluation of the intracranial venous system. REVIEW SUMMARY: This review provides an overview of the current noninvasive methods and their applications and limitations, with examples of their use in a variety of disease processes. Computed tomography venography (CTV) is discussed as well as the various types of cerebral magnetic resonance venography (MRV). CONCLUSION: When available, MR supplemented with the technique of triggered gadolinium-enhanced MRV is the method of choice for the diagnosis of dural sinus thrombosis as well as most other pathologic entities affecting the intracranial venous system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".