Ex vivo sentinel lymph node biopsy in colorectal cancer: A feasibility study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sentinel lymph node (SLN) biopsy may improve staging of colorectal cancer. We tested the feasibility of ex vivo SLN dissection. MATERIALS AND METHODS: Patients undergoing resection of a primary colorectal cancer were included in this study. SLN identification involved ex vivo injection of 1 cc isosulfan blue dye subserosally in the colon or submucosally in the rectum on a separate field. SLNs were cut at 2 mm intervals. Three hematoxylin and eosin-stained (HE) sections were prepared in addition to a middle level for cytokeratin immunostaining. RESULTS: Twenty-six patients with varying tumor location and stage were enrolled and the SLN was identified in 88% (23/26) cases. Three failures occurred in patients with rectal cancer. The average number of SLN harvested was 2.5. The status of the nodal basin was accurately predicted in 91% (21/23) of patients. Two false negative sentinel lymph nodes were harvested in 2 of 3 patients with stage III/IV colorectal cancer. The SLN upstaged 2 patients as a result of HE stained step sections (n = 1) and immunostaining (n = 1). CONCLUSIONS: This data suggests that ex vivo SLN biopsy is feasible in colorectal cancer. Although ex vivo SLN biopsy does not alter the lymphatic dissection, it may upstage a subset of patients. The ex vivo technique may be less applicable in rectal cancer and false negative results may occur.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".