An Evaluation of the Relationship Between Lymph Node Number and Staging in pT3 Colon Cancer Using Population-Based Data
Bibliographic record
Abstract
PURPOSE: The number of lymph nodes examined has been proposed as a quality benchmark for colon cancer surgery, although it is unknown whether this strategy reduces understaging. METHODS: We identified 11,044 patients who underwent surgery for colon cancer with pT3 wall penetration between 1988 and 2003 from the Surveillance, Epidemiology and End Results cancer registry. We determined the proportion of patients who were node positive for each node count. We used logistic regression to predict the odds of being node positive by node count after adjusting for confounders. We used joinpoint analysis to determine whether there was a consistent relationship between node count and the odds of being node positive. RESULTS: The proportion of patients found to be node positive increased with node count at low counts (<or=5-6 nodes), but patients with 7 nodes identified were as likely to be node positive as patients with 30 or more nodes (odds ratio = 0.97; 95% CI = 0.90-1.05). Joinpoint analysis demonstrated a dramatic increase in odds of node positivity with increasing node count to 5 nodes (slope = 0.2; P < .0001). Between 6 and 13 nodes there was a marginal increase in odds of positive nodes (slope = 0.03; P = .006), but when more nodes were evaluated, odds of node positivity actually declined (slope = -0.01; P = .04). CONCLUSIONS: Staging of pT3 colon cancer improves with increasing node count, but only when the node count is low (<5-7 nodes). At higher counts, an increased node count has marginal effects on staging.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".