Improving Margins of Resection in Surgical Oncology with the Intelligent Surgical Knife
Bibliographic record
Abstract
Margins of resection in surgical oncology are often a trade-off between decreasing cancer recurrence and preserving a patient’s aesthetic and function. The intelligent surgical knife (iKnife) aims to provide margins of resection in real-time leading to improved clinical outcomes while giving surgeons the confidence to excise tumors with smaller margins of resection. The iKnife utilizes mass spectrometry to identify the lipid and protein profiles of cells as they are cut with an electric cauterization tool. Through techniques such as multivariate analysis, proprietary software can discern healthy and cancerous tissue without the need for histological sectioning and staining. The effectiveness of the iKnife has been demonstrated ex vivo and in vivo with several types of tumors such as breast, ovarian, and colon cancer. The iKnife presents an exciting novel tool in the field of surgical oncology with the ability to provide an avenue to personalized medicine in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".