Part II: an evaluation of an integrated systems approach using diffusion-weighted, image-guided, exoscopic-assisted, transulcal radial corridors
Bibliographic record
Abstract
Abstract Background: Subcortical injury resulting from the surgical access and management of lesions in the sensorimotor area is associated with a high degree of cognitive and functional morbidity. Methods: We used a systems approach integrating the six core competencies of the 6 Pillar approach: 1) image interpretation and trajectory planning; 2) dynamic navigation; 3) radial transulcal access and cannulation; 4) exoscopic high-definition optics; 5) resection with automated nonthermal mechanical instrumentation; and 6) regenerative medicine. We describe the application of the 6 Pillar approach to 13 consecutive patients with lesions in the sensorimotor area. Results: Eight females and five males with lesions in the sensorimotor area were treated using the 6 Pillar approach. There were eight tumors, one abscess, and four primary intracranial hemorrhages. Fifteen procedures were performed. Postoperatively, seven patients improved neurologically (three tumors, one abscess, and three ICHs), five remained unchanged, and one patient died. There was no worsening of pre-existing deficits. Conclusion: The integration of the 6 Pillar approach provides a safe and effective parafascicular minimally invasive corridor to subcortical lesions involving the sensorimotor area. Future studies will be needed to determine long-term efficacy, durability, and degree of resection within each category.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".