Comparison of two novel staging systems with the TNM system in predicting stage III colon cancer survival
Bibliographic record
Abstract
Background and Objectives Adaptations of the TNM staging system that incorporate the Lymph Node Ratio (LNR) have been proposed for stage III colon cancer. This study compared the concordance of two novel staging systems and the TNM system with observed survival outcomes in stage III patients. Methods A review of patients who underwent surgery for stage III colon cancer between January 2002 and April 2015 at a tertiary care centre was performed. The Kaplan‐Meier method was used to estimate the 5‐year overall (OS) and disease free survival (DFS) rates, and the concordance probability was calculated to evaluate the discriminatory power of the staging systems. Results Two hundred and sixty‐one patients were identified. For TNM stages IIIA, IIIB, and IIIC, 5‐year OS was 83.4%, 67.6%, and 38.3%, respectively (P < 0.001). All three staging systems were independently predictive of OS and DFS (P < 0.001). However, the novel staging system by Sugimoto et al18 was the most favourable prognostic tool, with a concordance of 0.646 for DFS and 0.659 for OS. Conclusions The novel staging system by Sugimoto et al18 was superior to the TNM system. Incorporating LNR into staging models for node positive colon cancers may improve survival information available to patients and potentially aid treatment decisions.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".