428 Artificial Intelligence Training Versus In-person Expert Training in Teaching Simulated Tumor Resection Skills - A Cross-Over Randomized Controlled Trial
Bibliographic record
Abstract
INTRODUCTION: Neurosurgical simulations equipped with artificial intelligence systems provide an objective quantitative assessment of surgical technical skills and tailored intelligent feedback. These systems allow for repeated practice of complex skills such as subpial brain tumor resection. However, more work is needed to assess the utility of intelligent systems in teaching tumor resection skills, compared to traditional human instruction. METHODS: Twenty-five trainees who are currently enrolled in four Canadian medical schools participated in two training sessions, during which they completed a simulated subpial tumor resection five times. Participants were randomly assigned to two feedback groups: (1) real-time intelligent instruction, and (2) in-person human instruction. They were then assigned to the other feedback group in the second (cross-over) session. A composite-score was given by the intelligent system to assess technical skills during each task. RESULTS: Trainees who received real-time intelligent instruction significantly improved their composite-score in the first and second training sessions (p = .017, p = .005, respectively). Trainees who received in-person human instruction in the first training session had no statistically significant changes in their composite-score from the first to the last task repetition (p = .119). The composite-score decreased significantly in the second training session with in-person human instruction (p = .004). CONCLUSIONS: Real-time intelligent instruction provides an effective way of teaching simulated brain tumor resection skills. Artificial intelligence systems may be a useful addition to current surgical training curricula, providing objective, and tailored assessment and teaching of surgical technical skills in risk-free realistically simulated patient cases. Educators can benefit from these systems to guide their supervision, while trainees can use them for self-guided skill acquisition. Further research may assess the generalizability of these findings to other surgical procedures.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".