Additional file 1 of A gene mutation-based risk model for prognostic prediction in liver metastases
Bibliographic record
Abstract
Additional file 1: Fig. S1. The summary of patients included in the training (a), validation (b) and primary liver cancer (c) and transcriptomic (d) cohorts. Fig. S2. Assessment of the predictive performance of the gene mutation-based risk model. a. Receiver operating characteristic (ROC) curve of the training cohort. b. ROC curve of the validation cohort. c. Area under the ROC curve (AUC) for different cancer types in the training cohort. d. AUC for different cohorts in the validation cohort. Fig. S3. Assessment of the predictive accuracy of the gene mutation-based risk model. a. Calibration curve for assessing the predictive accuracy of the gene mutation-based risk model in the training cohort. The gray line represents ideal performance. The red line represents actual performance. b. Calibration curve for assessing the predictive accuracy of the gene mutation-based risk model in the validation cohort. The gray line represents ideal performance. The red line represents actual performance. c. Univariate and multivariate Cox regression analyses of gene mutation-based risk model in the training cohort. d. Univariate and multivariate Cox regression analyses of gene mutation-based risk model in the validation cohort. e. Comparison of C-indexes between the gene mutation-based risk model and 10 gene mutations included in the risk model in the training cohort. f. Comparison of C-indexes between the gene mutation-based risk model and 10 gene mutations included in the risk model in the validation cohort.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.775 | 0.084 |
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".