Bibliographic record
Abstract
When a woman is diagnosed with breast cancer, several treatment options are considered including breast conserving surgery. In this type of surgery, the goal is to completely remove the cancer while leaving as much healthy breast tissue as possible. This is a clinical judgement of high consequence since resecting less tissue is cosmetically appealing but increases the chances of leaving cancer cells behind, known as a positive margin. Conventionally, this operation is performed with an electrocautery – imagine it as an electronic knife – which seals tissue as it cuts and produces small amounts of surgical smoke in the process. In most operating rooms today this smoke is treated as a by product, and it is discarded with no further consideration. But this smoke is rich with useful information; it contains traces of the molecules the knife passed through when the smoke was generated. The intelligent knife (iKnife) analyzes this smoke to determine the pathology of tissue the surgeon’s knife has passed through – whether the tissue is cancerous or not. We have coupled the iKnife with an electromagnetic position tracking system to create a three dimensional spatially resolved malignancy map showing where the surgeon’s knife has encountered cancerous tissue. We have developed a functional prototype and have approval for a first clinical safety and feasibility trial. We hope the spatial map will help surgeons to successfully remove the entire malignancy with the smallest amount of healthy tissue while maintaining negative margins – a successful surgical outcome for the patient.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".