Analytic lymph node number establishes staging accuracy by occult tumor burden in colorectal cancer
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Recurrence in lymph node-negative (pN0) colorectal cancer suggests the presence of undetected occult metastases. Occult tumor burden in nodes estimated by GUCY2C RT-qPCR predicts risk of disease recurrence. This study explored the impact of the number of nodes analyzed by RT-qPCR (analytic) on the prognostic utility of occult tumor burden. METHODS: Lymph nodes (range: 2-159) from 282 prospectively enrolled pN0 colorectal cancer patients, followed for a median of 24 months (range: 2-63), were analyzed by GUCY2C RT-qPCR. Prognostic risk categorization defined using occult tumor burden was the primary outcome measure. Association of prognostic variables and risk category were defined by multivariable polytomous and semi-parametric polytomous logistic regression. RESULTS: Occult tumor burden stratified this pN0 cohort into categories of low (60%; recurrence rate (RR) = 2.3% [95% CI 0.1-4.5%]), intermediate (31%; RR = 33.3% [23.7-44.1%]), and high (9%; RR = 68.0% [46.5-85.1%], P < 0.001) risk of recurrence. Beyond race and T stage, the number of analytic nodes was an independent marker of risk category (P < 0.001). When >12 nodes were analyzed, occult tumor burden almost completely resolved prognostic risk classification of pN0 patients. CONCLUSIONS: The prognostic utility of occult tumor burden assessed by GUCY2C RT-qPCR is dependent on the number of analytic lymph nodes.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".