Angiographic Evaluation of Cranial Venous Outflow Patterns in Patients With and Without Idiopathic Intracranial Hypertension
Bibliographic record
Abstract
BACKGROUND: Several collateral venous pathways exist to assist in cranial venous drainage in addition to the internal jugular veins. The important extrajugular networks (EJN) are often readily identified on diagnostic cerebral angiography. However, the angiographic pattern of venous drainage through collateral EJN has not been previously compared among patients with and without idiopathic intracranial hypertension (IIH). OBJECTIVE: To quantify EJN on cerebral angiography among patients both with and without IIH and to determine whether there is a different EJN venous drainage pattern in patients with IIH. METHODS: Retrospective imaging review of 100 cerebral angiograms (50 IIH and 50 non-IIH patients) and medical records from a single academic medical center was performed by 2 independent experienced neuroendovascular surgeons. Points were assigned to EJN flow from 0 to 6 using an increasing scale (with each patient's dominant internal jugular vein standardized to 5 points to serve as the internal reference). Angiography of each patient included 11 separately graded extrajugular networks for internal carotid and vertebral artery injections. RESULTS: Patients in the IIH group had statistically significant greater flow in several of the extrajugular networks. Therefore, they preferentially drained through EJN compared with the non-IIH group. Right transverse-sigmoid system was most often dominant in both groups, yet there was a significantly greater prevalence of codominant sinus pattern on posterior circulation angiograms. CONCLUSION: Patients with IIH have greater utilization of EJN compared with patients without IIH. Whether this is merely an epiphenomenon or possesses actual cause-effect relationships needs to be determined with further studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".