Pooled Analysis of Fluorouracil-Based Adjuvant Therapy for Stage II and III Colon Cancer: Who Benefits and by How Much?
Bibliographic record
Abstract
PURPOSE: Although it is well-established that fluorouracil- (FU-) based adjuvant therapy improves survival for patients with resected high-risk colon cancer, the magnitude of adjuvant therapy benefit across specific subgroups and for individual patients has been uncertain. PATIENTS AND METHODS: Using a pooled data set of 3,302 patients with stage II and III colon cancer from seven randomized trials comparing FU + leucovorin or FU + levamisole to surgery alone, we performed an analysis based on a Cox proportional hazards regression model. Treatment, age, sex, tumor location, T stage, nodal status, and grade were tested for both prognostic and predictive significance. Model derived estimates of 5-year disease-free survival and overall survival (OS) for surgery alone and surgery plus FU-based therapy were calculated for a range of patient subsets. RESULTS: Nodal status, T stage, and grade were the only prognostic factors independently significant for both disease-free survival and OS. Age was significant only for OS. In a multivariate analysis, adjuvant therapy showed a beneficial treatment effect across all subsets. Treatment benefits were consistent across sex, location, age, T-stage, and grade. A significant stage by treatment interaction was present, with treatment benefiting stage III patients to a greater degree than stage II patients. CONCLUSION: Patients with high-risk resected colon cancer obtain benefit from FU-based therapy across subsets of age, sex, location, T stage, nodal status, and grade. Model estimates of survival stratified by T stage, nodal status, grade, and age are available at http://www.mayoclinic.com/calcs. This information may improve patients' and physicians' understanding of the potential benefits of adjuvant therapy.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".