Improving the prediction of colon cancer survival after curative resection.
Bibliographic record
Abstract
552 Background: Cancer staging systems convey valuable prognostic information to both clinicians and patients. Currently, colon cancer is staged according to the American Joint Committee on Cancer (AJCC) TNM classification system. However, survival estimates for patients with the same stage of colon cancer may vary considerably due to other factors including age, sex, grade, and number of lymph nodes sampled. The objectives of this study are to 1) assess the accuracy of the seventh edition of the TNM classification system in predicting survival of patients with primary colon cancer after curative-intent surgery, and 2) evaluate the utility of incorporating additional demographic and tumor variables beyond TNM staging in improving prognostic accuracy. Methods: Patients with curative-intent resection of a first primary adenocarcinoma of the colon at the time of referral to the Cross Cancer Institute between 2004 and 2007 were identified from the Alberta Cancer Registry. We constructed three multivariate Cox’s proportional hazard models to explore the effect of supplementing TNM staging with additional demographic and tumor variables in predicting overall survival (OS). Results: 559 consecutive patients with complete chart records were identified. 52 % (n=290) were male; median age was 74. In the first model based only on T and N elements, N2 disease was correlated with increased mortality (hazard ratio [HR], 2.546; p<0.0001). When the number of lymph nodes examined (HR, 0.980; p=0.034) and number of metastatic lymph nodes detected (HR, 1.094; p<0.0001) were substituted for the N-staging element, both variables correlated positively and negatively with outcome, respectively. Finally, when tumor grade, sex and age were incorporated into the model, number of examined lymph nodes (HR, 0.980; p=0.029) and those containing tumor (HR, 1.093; p<0.0001) remained independent predictors of OS. Conclusions: Incorporating readily available demographic and tumor variables, such as age, sex and number of lymph nodes examined, can enhance the current TNM staging system and improve prognostication in early stage colon cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".