Human versus machine: are neurointerventionists more precise in manual or robotically assisted procedures?
Bibliographic record
Abstract
BACKGROUND: Robotic neurointerventions have demonstrated promising initial clinical results. Claims of enhanced precision during robotic navigation have been reported, but objective quantification of such precision is limited. Precision during intracranial navigation and device deployment is crucial in neurovascular interventions, and lack of precision can lead to intraprocedural complications. This study compared quantitative metrics of precision in manual and robotic procedures using a virtual simulator. METHODS: Using three different simulated aneurysm procedures with different levels of difficulty (easy, medium, and hard), 12 operators with different levels of experience were assigned a defined task for each case. Each procedure was performed both manually and under robotic assistance. Precision was assessed using the length of translations and the total degree of rotations of the microwire and microcatheter needed to complete the assigned tasks, as well as recorded safety metrics. Results were compared between the manual and robotic groups. RESULTS: We analyzed 78 procedures (robotic, n=34; manual, n=34) performed by 12 operators with various levels of neurointerventional surgical experience (high, n=5; low, n=7). For the difficult case, operators used significantly less microwire translations when operating with robotic assistance (38.7 cm vs 108.4 cm, P=0.023). There were no significant differences for the easy and medium cases. Safety metrics and procedural times were not significant different. CONCLUSIONS: Operators demonstrated increased precision during microwire navigation when using robotic assistance to navigate a difficult aneurysm in a controlled simulated experimental set-up compared with manual navigation.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".