Transverse Venous Sinus Stenosis in Idiopathic Intracranial Hypertension – A Prospective Pilot Study
Bibliographic record
Abstract
Abstract Objective Whether transverse venous sinus stenosis (TVSS) causes idiopathic intracranial hypertension (IIH) or is an effect of the increased intracranial pressures is controversial. The purpose of this study was to assess the feasibility of serial imaging in patients with IIH on medical management. Design and Participants Patients found to have IIH and TVSS on contrast-enhanced magnetic resonance venography (CEMRV) were recruited in a prospective cohort study. Patients were medically managed and followed with a CEMRV immediately after lumbar puncture, 3–6 months after diagnosis with resolution of IIH symptoms, and 1 year after diagnosis. Ophthalmological data were collected at the time of diagnosis, 3–6 months after diagnosis, and 1 year after diagnosis. Feasibility data, including patient recruitment rate, barriers, and logistical issues, were recorded. Results Twenty patients with suspected IIH were screened, and 5 of 7 (71.4%; 95% confidence interval: 36.21–100) eligible patients were enrolled in 1 year, at completion. All recruited patients had clinical resolution of their IIH on medical therapy, and none of them had any obvious change in their TVSS. Conclusions Prospective examination of TVSS with serial magnetic resonance imaging in patients with IIH is feasible. TVSS in patients with IIH did not show any change, despite clinical improvement on medical management in all participants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".