Combined Exoscopic and Endoscopic Two-Step Keyhole Approach for Intracranial Meningiomas
Bibliographic record
Abstract
The advantages of neuroendoscopic surgery are the wide viewing angle and the freedom of an axis of view with minimal surgical trauma. With the advent of the exoscope, which has similar advantages to endoscopy, such as a small body and ergonomically superior heads-up surgery, it has become possible to add a field of view that is similar to that of microsurgery to endoscopic surgery. By taking advantage of the features of these scopes, we report the usefulness of the minimally invasive combined exoscopic and endoscopic two-step keyhole approach (EEKA) for various types of meningiomas. We reviewed data from 34 consecutive cases of EEKA for various types of intracranial meningiomas compared with that of conventional microsurgery. All of the tumors were resected as planned without severe complications. Significantly better outcome data were obtained in terms of the blood loss and the surgical time in the EEKA group, in addition to the craniotomy size. The well-illuminated fine vision in the deep corners by the endoscope enabled radical resection of the tumors with minimum burden on the patients. This technique has the potential for minimally invasive surgery in intracranial meningioma patients, including the older population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".