Impact of lymph node retrieval and pathological ultra‐staging on the prognosis of stage II colon cancer
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A minimum number of lymph nodes must be assessed for accurate diagnosis of stage II colon cancer. We assessed number of lymph nodes retrieved, pathological ultra-staging, and outcome in stage II colon cancer. MATERIALS AND METHODS: Consecutively treated patients with stage II colon cancer were identified. Baseline and outcome data were collected. Retrospective ultra-staging using lymphovascular invasion (LVI) and nodal micrometastases was performed. Patients were divided into two groups: group I had 6 nodes retrieved. Survival was analyzed. RESULTS: One hundred and fifteen patients were included in the study. The 5 year overall survival was worse in group I versus II (P = 0.03). LVI and micrometastases were identified but neither predicted survival. Disease failure in group I was due to distant metastases rather than local recurrence. CONCLUSIONS: Inadequate retrieval and assessment of lymph nodes is associated with worse outcome in stage II colon cancer patients. Recurrence patterns support the hypothesis that disease recurrence occurred due to inaccurate staging. In this small study, LVI or nodal micrometastases did not predict survival. Maximal attention should be paid to the total number of lymph nodes retrieved before embarking on potentially more resource intensive staging methods.
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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 | 0.005 |
| 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) | 0.001 | 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".