Stereotactic Frame-Based Targeting of the Posterior Fossa: A Systematic Workflow for the Leksell G Frame
Bibliographic record
Abstract
INTRODUCTION: Cerebellar deep brain stimulation (DBS) is gaining traction as a potential treatment for movement disorders and stroke, and there is renewed interest in the cerebellum as a target for neuromodulation. Despite the safety and accuracy of frame-based approaches to the posterior fossa, unconventional stereotactic frame placement may be necessary to allow for low posterior fossa trajectories. Current literature lacks a comprehensive protocol detailing inverted frame placement and targeting. METHODS: Preoperative imaging was acquired prone. An inverted Leksell G frame was applied along with an open-topped CT fiducial box, followed by a prone CT with the scanner set to the "legs first, nose up" configuration. Target coordinates were extracted from navigation software after image fusion. Intraoperatively, the patient was positioned prone, and the stereotactic arc was mounted in the lateral-right orientation, with inverted arc supports. Confirmatory stereotaxy to a scalp staple was performed, and the DBS leads were then inserted. CONCLUSION: Our standardized protocol provides a flexible platform for posterior fossa DBS, allowing for low trajectories and multiple electrodes. Unlike conventional upright frame placement, an inverted frame permits an unobstructed view of suboccipital entry sites and incision placement. A conventional frame and regular planning software are sufficient, with no additional mathematical calculations required.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".