Sentinel Node Staging of Resectable Colon Cancer
Bibliographic record
Abstract
OBJECTIVE AND SUMMARY BACKGROUND DATA: Sentinel lymph node (LN) sampling, a technique widely used to manage breast cancer and melanoma, seeks to select LNs that accurately predict regional node status and can be extensively examined to identify nodal metastatic disease not detected by standard histopathological staging. For patients with resectable colon cancer, improved identification of LN disease would significantly advance patient care by identifying patients likely to benefit from adjuvant therapy. This study, conducted by 25 surgeons at 13 institutions, examined whether sentinel node (SN) sampling accurately predicted LN status for patients with resectable colon cancer. METHODS: SN sampling involved peritumor injection of 1% isosulfan blue, followed by identification of all LN visualized within 10 minutes. SN sampling was performed on 79 of 91 patients enrolled, followed by multilevel sectioning (MLS) of the nodes and examination by a single study pathologist. RESULTS: By standard histopathology, 7 patients had primary disease that was either benign or not colon cancer and were therefore excluded from further studies. Of 72 colon cancer cases studied, 48 (66%) were node-negative and 24 (33%) contained nodal metastases. SNs were successfully located in 66 cases (92%), with an average of 2.1 nodes per patient. SNs were negative in 14 of 24 node-positive cases (58%). MLS revealed tumor in a SN in 1 of these cases, bringing the false-negative rate of SN examination to 54%. CONCLUSION: This multi-institutional study found that for patients with node-positive colon cancer, SN examination with MLS failed to predict nodal status in 54% of cases. We conclude that SN sampling with MLS, used alone, is unlikely to improve risk stratification for resectable colon cancer.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".