The Interpeduncular Angle: A Practical and Objective Marker for the Detection and Diagnosis of Intracranial Hypotension on Brain MRI
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Classic findings of intracranial hypotension on MR imaging, such as brain stem slumping, can be variably present and, at times, subjective, potentially making the diagnosis difficult. We hypothesize that the angle between the cerebral peduncles correlates with the volume of interpeduncular cistern fluid and is decreased in cases of intracranial hypotension. We aimed to investigate its use as an objective assessment for intracranial hypotension. MATERIALS AND METHODS: Brain MRIs of 30 patients with intracranial hypotension and 30 age-matched controls were evaluated by 2 fellowship-trained neuroradiologists for classic findings of intracranial hypotension and the interpeduncular angle. Group analysis was performed with a Student t test, and receiver operating characteristic analysis was used to identify an ideal angle threshold to maximize sensitivity and specificity. Interobserver reliability was assessed for classic findings of intracranial hypotension using the Cohen κ value, and the interpeduncular angle, using the intraclass correlation. RESULTS: The interpeduncular angle had excellent interobserver reliability (intraclass correlation coefficient value = 0.833) and was significantly lower in the intracranial hypotension group compared with the control group (25.3° versus 56.3°; P < .001). There was significant correlation between the interpeduncular angle and the presence of brain stem slumping (P < .001) and in cases with ≥3 classic features of intracranial hypotension (P = .01). With a threshold of 40.5°, sensitivity and specificity were 80% and 96.7%, respectively. CONCLUSIONS: The interpeduncular angle is a sensitive and specific measure of intracranial hypotension and is a reliably reproducible parameter on routine clinical MR imaging.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".