Robotic Stereotaxy in Cranial Neurosurgery: A Qualitative Systematic Review
Bibliographic record
Abstract
BACKGROUND: Modern-day stereotactic techniques have evolved to tackle the neurosurgical challenge of accurately and reproducibly accessing specific brain targets. Neurosurgical advances have been made in synergy with sophisticated technological developments and engineering innovations such as automated robotic platforms. Robotic systems offer a unique combination of dexterity, durability, indefatigability, and precision. OBJECTIVE: To perform a systematic review of robotic integration for cranial stereotactic guidance in neurosurgery. Specifically, we comprehensively analyze the strengths and weaknesses of a spectrum of robotic technologies, past and present, including details pertaining to each system's kinematic specifications and targeting accuracy profiles. METHODS: Eligible articles on human clinical applications of cranial robotic-guided stereotactic systems between 1985 and 2017 were extracted from several electronic databases, with a focus on stereotactic biopsy procedures, stereoelectroencephalography, and deep brain stimulation electrode insertion. RESULTS: Cranial robotic stereotactic systems feature serial or parallel architectures with 4 to 7 degrees of freedom, and frame-based or frameless registration. Indications for robotic assistance are diversifying, and include stereotactic biopsy, deep brain stimulation and stereoelectroencephalography electrode placement, ventriculostomy, and ablation procedures. Complication rates are low, and mainly consist of hemorrhage. Newer systems benefit from increasing targeting accuracy, intraoperative imaging ability, improved safety profiles, and reduced operating times. CONCLUSION: We highlight emerging future directions pertaining to the integration of robotic technologies into future neurosurgical procedures. Notably, a trend toward miniaturization, cost-effectiveness, frameless registration, and increasing safety and accuracy characterize successful stereotactic robotic technologies.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".