Additional file 2 of Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling
Bibliographic record
Abstract
Additional file 2: Table S1. Cytogenetic classifications at the time of ALL diagnosis for the patients in the training and test sets. Table S2. C-indexes of the different random forest models evaluated for the prediction of RFS and OS. Table S3. Variable importance values for each of the CpGs in the relapse risk predictor (RRP). CpGs are listed in decreasing order of importance. Table S4. Patient distribution across the low- and high- relapse risk or mortality risk predictor (RRP/MRP) groups after applying cut-offs on the train, test and independent datasets. a) Low and high-RRP groups in response to relapse as outcome, b) low and high-MRP groups in response to relapse as outcome and c) low and high-MRP groups in response to death as outcome. Univariate cox regression was conducted to assess the effect of the RRP/MRP-based dichotomization on patient outcome. Table S5. Variable importance values for each of the CpGs in the final mortality risk predictor (MRP). Variables are depicted in decreasing order of importance. Table S6. Revised molecular subtype annotation analyzed by Krali et al. Table S7. C-indexes of the MRP in the independent dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.745 | 0.102 |
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".