Additional variables other than AJCC staging show clinical utility for predicting survival in colon cancer
Bibliographic record
Abstract
14584 Background: The most important predictor of colon cancer patient outcome is disease stage at the time of surgery. However, staging does not accurately predict survival for all patients undergoing a resection with curative intent. The aim of this study was to analyze clinical and pathological characteristics of patients undergoing curative colon cancer, in order to identify characteristics, in addition to stage, predictive of disease outcome. Methods: Between 1997 and 2005 data for 114 patients undergoing curative resection for colon cancer at a tertiary care institution were collected. Clinical and pathological characteristics evaluated were: age, gender, tumor location, tumor size, scheduled vs emergent surgery, pathologic margin status, TNM stage, pathologic grade, number of positive lymph nodes, total number of lymph nodes resected, vascular and lymphatic invasion. Characteristics found to be significant in a Kaplan-Meier univariate survival analysis were included in a multivariate stepwise logistic regression analysis. Patient outcomes studied were overall survival, cancer specific survival, and disease free survival. Results: From the 114 patients examined in this cohort the mean age was 67 years, the male to female ratio was 0.8:1, and the mean follow up time was 2.61 years. Overall survival, cancer specific survival, and disease free survival were calculated to be 83.3%, 91.2% and 84.2%, respectively. Statistically significant variables by univariate analysis were: AJCC stage, number of positive lymph nodes, pathologic N stage, lymphatic and vascular invasion by the primary tumor. Further multivariate analysis revealed that lymphatic invasion was the only significant independent influence for predicting disease recurrence. Conclusions: Clinicopathologic characteristics, in addition to AJCC disease stage, may be of clinical utility in predicting outcome for patients who have undergone curative resection for colon cancer. Further evaluation of these clinicopathologic characteristics should be carried out in a larger colon cancer patient cohort. No significant financial relationships to disclose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".