Intraoperative Assessment of Breast Lymph Nodes using Cancer Diagnostic Probe in Impedimetric Mode
Bibliographic record
Abstract
Axillary lymph node dissection (ALND) was a standard component of breast cancer surgery for decades until the 1990s, when sentinel lymph node biopsy (SLNB) emerged as a less invasive and more precise alternative.SLNB revolutionized axillary staging by reducing complications and unnecessary dissections.Today, it is the preferred method in most breast cancer surgeries, rendering ALND nearly obsolete in many clinical scenarios (1).Initially, intraoperative assessment of sentinel lymph nodes was pivotal in determining the need for immediate ALND.This reduced the need for reoperations in patients with positive nodes.However, as the role of ALND declined, so did the emphasis on intraoperative node evaluation (2).Currently, ALND is reserved for select cases, such as clinically node-positive patients receiving neoadjuvant chemotherapy (NAC) (3) or luminal breast cancer patients with three or more involved nodes.In patients with only one or two positive nodes, ALND is generally avoided, and radiation therapy is often employed.However, current diagnostic techniques sometimes struggle to accurately quantify involved nodes, especially in cases involving small or confluent metastases (4).With the increased use of NAC, particularly in countries where patients present at more advanced stages, accurate lymph node evaluation remains essential.Traditional intraoperative diagnostic techniques, such as frozen section (FS) and touch preparation, although generally effective in luminal ductal carcinoma, have limited value post-NAC and in invasive lobular carcinoma (ILC).Moreover, FS prolongs surgery by 45 minutes to an hour, thereby increasing anesthesia duration and straining hospital resources (5).Thus, a critical clinical gap remains: current intraoperative methods are time-intensive, exhibit reduced sensitivity in post-NAC and ILC patients, and may be impractical in resource-limited settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".