Ex vivo sentinel lymph node biopsy in colorectal cancer: A feasibility study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it