The lion's mane sign: surgical results using the bilateral fronto-orbito-nasal approach in large and giant anterior skull base meningiomas
Bibliographic record
Abstract
OBJECT: Concerns about extreme peritumoral edema and its ensuing surgical and perioperative complications led the authors to use the bilateral fronto-orbito-nasal approach to remove midline anterior skull base meningiomas that were larger than 4 cm. The authors hypothesize that extreme vasogenic edema exemplified by finger-like hyperintensities extending into the bifrontal white matter and external capsule and/or the extreme capsule, coined the lion's mane sign (LMS), would help identify patients with a challenging postoperative course. They hypothesize that the LMS would better predict symptomatic postoperative cerebral edema than the edema index (EI). METHODS: This is an observational case series of 9 patients. The authors noted the grade, pathology, tumor volume, EI, and the presence or absence of the LMS in all tumors. They used the intensive unit care (ICU) length of stay as a nonspecific measure reflecting postoperative symptomatic cerebral edema. Comparisons of edema-related postoperative complications and the EI were made between patients with and without an LMS. RESULTS: Bifrontal hyperintensities, extending into at least three-eighths of the length of the external capsules on T2-weighted MRI, seen in 4 of 9 patients, portended a longer postoperative ICU stay. The presence of an LMS better predicted postoperative complications related to cerebral edema than tumor grade, pathology, volume, or EI. CONCLUSIONS: The LMS predicts an increased duration of stay in the ICU after a bilateral fronto-orbito-nasal approach for resection of large and giant anterior skull base meningiomas. Furthermore, the LMS better predicted increased length of stay in the ICU than did the EI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".