Increasing Negative Lymph Node Count Is Independently Associated With Improved Long-Term Survival in Stage IIIB and IIIC Colon Cancer
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the impact of the number of negative lymph nodes on survival in patients with stage III colon cancer. PATIENTS AND METHODS: Patients who underwent surgery for stage III colon cancer between January 1988 and December 1997 were identified from the Surveillance, Epidemiology and End Results cancer registry. The number of negative and positive nodes was determined for 20,702 eligible patients. Disease-specific survival was examined by substage according to the number of negative nodes identified. A proportional hazards model was constructed to determine the effect of the number of negative nodes on survival. RESULTS: For stage IIIB and IIIC patients, there was a significant decrease in disease-specific mortality as the number of negative nodes increased; cumulative 5-year cancer mortality was 27% in stage IIIB patients with 13 or more negative nodes identified versus 45% in those with three or fewer negative lymph nodes evaluated (P < .0001). In patients with stage IIIC cancer, those with 13 or more negative nodes had a 5-year mortality of 42% versus 65% in those with three or fewer negative lymph nodes evaluated (P < .0001). There was no association between the number of negative nodes identified and disease-specific survival for patients with stage IIIA disease. After controlling for the number of positive nodes, a higher number of negative nodes was found to be independently associated with improved disease-specific survival. CONCLUSION: The number of negative nodes is an important independent prognostic factor for patients with stage IIIB and IIIC colon cancer.
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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 | 0.000 | 0.002 |
| 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".