The accuracy of three predictive models in the evaluation of recurrence rates for gastrointestinal stromal tumors
Bibliographic record
Abstract
BACKGROUND: Treatment decisions for gastrointestinal stromal tumors (GIST) are frequently guided by tumor characteristics. An accurate prediction of recurrence is important to determine the benefit from targeted therapy. Our goal was to compare the concordance of three validated risk stratification schemes with observed outcomes in patients undergoing resection for GISTs. METHODS: Patients who underwent surgery for GISTs from 2001 to 2011 at a tertiary centre were identified. Survival was evaluated using the Kaplan-Meier product-limit method. Cox proportional hazard models were used to obtain predicted recurrence for each system and concordance indices were calculated. RESULTS: Of 110 patients identified, 77 (70.0%) had surgery and 29 (26.4%) also received adjuvant therapy. The majority of patients had tumors that were very low (4.5%), low (32.7%), or intermediate (22.7%) in terms of malignant potential. R0 resection was achieved in 89.1% of cases. Observed 2-year and 5-year recurrence rates were significantly lower than those predicted by the Memorial Sloan Kettering Cancer Center nomogram (7.6% vs. 19.3% and 18.4% vs. 27.0%); however, it was the most favorable tool compared to the US National Institutes of Health (NIH)-consensus (P = 0.0017) and modified NIH-consensus (P < 0.001), with a concordance index of 0.811. CONCLUSION: Development of a novel predictive tool that includes additional prognostic factors may better stratify recurrence following resection for GIST.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".