Tumor Microsatellite Instability and Clinical Outcome in Young Patients with Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer can arise through two distinct mutational pathways: microsatellite instability or chromosomal instability. We tested the hypothesis that colorectal cancers arising from the microsatellite-instability pathway have distinctive clinical attributes that affect clinical outcome. METHODS: We tested specimens of colorectal cancer from a population-based series of 607 patients (50 years of age or younger at diagnosis) for microsatellite instability. We compared the clinical features and survival of patients who had colorectal cancer characterized by high-frequency microsatellite instability with these characteristics in patients who had colorectal cancers with microsatellite stability. RESULT: We found high-frequency microsatellite instability in 17 percent of the colorectal cancers in 607 patients, and in a multivariate analysis, microsatellite instability was associated with a significant survival advantage independently of all standard prognostic factors, including tumor stage (hazard ratio, 0.42; 95 percent confidence interval, 0.27 to 0.67; P< 0.001). Furthermore, regardless of the depth of tumor invasion, colorectal cancers with high-frequency microsatellite instability had a decreased likelihood of metastasizing to regional lymph nodes (odds ratio, 0.33; 95 percent confidence interval, 0.21 to 0.53; P< 0.001) or distant organs (odds ratio, 0.49; 95 percent confidence interval, 0.27 to 0.89; P=0.02). CONCLUSION: High-frequency microsatellite instability in colorectal cancer is independently predictive of a relatively favorable outcome and, in addition, reduces the likelihood of metastases.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".