Lymph node counts, rates of positive lymph nodes, and patient survival for colon cancer surgery in Ontario, Canada: A population-based study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study assessed lymph node counts, lymph node status (positive or negative), and survival among patients undergoing colon cancer surgery in Ontario, Canada. METHODS: We obtained data from the Ontario Cancer Registry on 960 patients who underwent a major colon cancer resection in years 1991-1993. Patients and hospitals were ranked by lymph node count to correlate lymph node counts and lymph node status. For node-negative patients we assessed the influence of patient, hospital, and tumor factors on lymph node counts and survival. RESULTS: The rate of node-positive patients was similar among the lymph node count groups. For example, the odds ratio of a patient being node positive if the lymph node count was 10-36 versus 1-3 was 1.0 (CI 0.6-1.6, P = 0.42). Among node-negative patients, survival was improved for patients with a high (10-36) versus low (1-3) lymph node count (HR 0.6, CI 0.4-1.0, P = 0.03). No patient, hospital, or tumor factors predicted both a higher lymph node count and improved survival. CONCLUSIONS: In this population-based study of patients undergoing colon cancer surgery, higher lymph node counts did not correlate with increased rates of node-positive status.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".