Prognostic web‐based models for stage II and III colon cancer
Bibliographic record
Abstract
BACKGROUND: Numeracy and Adjuvant! are 2 web-based calculators that are used widely to estimate the prognosis and potential benefit of adjuvant 5-fluorouracil (5-FU)-based therapy for patients with stage II and III colon cancer. In this study, the authors compared the predicted survival estimates from these models with the actual observed estimates in independent datasets that were derived from a population cohort and from clinical trials. METHODS: The population cohort was derived from the British Columbia Colorectal Cancer Outcomes Unit database, which identified referred patients with stage II and III colon cancer from 1995 to 1996 and from 1999 to 2003. Patients who were enrolled in North Central Cancer Trials Group (NCCTG) trials NCCTG 94651 and NCCTG 914653 were included in the trials dataset. Patient and disease data were used to predict 5-year relapse-free and overall survival using both tools. RESULTS: In the population-based dataset (N = 2033), Adjuvant! offered more reliable predictions of prognosis for patients who underwent surgery alone, but it had reliability similar to that of Numeracy for predicting the prognosis for patients who received adjuvant 5-FU. Both models tended to overestimate survival for patients with stage II disease who received 5-FU. In the trials dataset of patients who underwent and received 5-FU (N = 1729), Numeracy and Adjuvant! demonstrated similar performance and improved correctness. CONCLUSIONS: This independent validation analysis demonstrated that both Numeracy and Adjuvant! had similar predictive performance and acceptable reliability for patients with stage III disease. Survival outcomes of patients with stage II colon cancer who received adjuvant 5-FU were slightly lower than estimated by either model.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".