Lymph Node Evaluation for Colon Cancer in Routine Clinical Practice: A Population-Based Study
Bibliographic record
Abstract
Background: Guidelines recommend that 12 or more lymph nodes (lns) be evaluated during surgical resection of colon cancer. Here, we report ln yield and its association with survival in routine practice. Methods: Electronic records of treatment were linked to the population-based Ontario Cancer Registry to identify all patients with colon cancer treated during 2002–2008. The study population (n = 5508) included a 25% random sample of patients with stage ii or iii disease. Modified Poisson regression was used to identify factors associated with ln yield; Cox models were used to explore the association between ln yield and overall (os) and cancer-specific survival (css). Results: During 2002–2008, median ln yield increased to 17 from 11 nodes (p < 0.001), and the proportion of patients with 12 or more nodes evaluated increased to 86% from 45% (p < 0.001). Lymph node positivity did not change over time (to 53% from 54%, p = 0.357). Greater ln yield was associated with younger age (p < 0.001), less comorbidity (p = 0.004), higher socioeconomic status (p = 0.001), right-sided tumours (p < 0.001), and higher hospital volume (p < 0.001). In adjusted analyses, a ln yield of less than 12 nodes was associated with inferior os and css for stages ii and iii disease [stage ii os hazard ratio (hr): 1.36; 95% confidence interval (ci): 1.19 to 1.56; stage ii css hr: 1.52; 95% ci: 1.26 to 1.83; and stage iii os hr: 1.45; 95% ci: 1.30 to 1.61; stage iii css hr: 1.54; 95% ci: 1.36 to 1.75]. Conclusions: Despite a temporal increase in ln yield, the proportion of cases with ln positivity has not changed. Lymph node yield is associated with survival in patients with stages ii and iii colon cancer. The association between ln yield and survival is unlikely to be a result of stage migration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".