Visual feedback mounted on surgical tool: proof of concept
Bibliographic record
Abstract
PURPOSE: When using surgical navigation systems in the operating room, feedback is typically displayed on a computer monitor. The surgeon’s attention is usually focused on the tool and the surgical site, so the display is typically out of the direct line of sight. The purpose is to develop a visual feedback device mounted on an electromagnetically tracked electrosurgical cauterizer which will provide navigation information for the surgeon in their field of view. METHODS: A study was conducted to determine the usefulness of the visual feedback in adjunct to the navigation system currently in use. Subjects were asked to follow tumor contours with the tracked cauterizer using 3D screen navigation with the mounted visual feedback and the 3D navigation screen alone. The movements of the cauterizer were recorded. RESULTS: The study showed a significant decrease in the subjects’ distance from the tumor margin, a significant increase in the subjects' confidence to avoid cutting the tumor and a statistically significant reduction in the subjects' perception of the need to look at the screen when using the visual feedback device compared to without. DISCUSSION: The LED feedback device helped the subjects feel confident in their ability to identify safe margins and minimize the amount of healthy tissue removed in the tumor resection. CONCLUSION: Good potential for the visual LED feedback has been shown. With additional training, this approach promises to lead to improved resection technique, with fewer cuts into the tumor and less healthy tissue removed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".