296 | LIMITED STAGE DIFFUSE LARGE B CELL LYMPHOMA IN THE MODERN ERA: REAL‐WORLD OUTCOMES IN AN INTERNATIONAL STUDY OF OVER ONE THOUSAND PATIENTS
Bibliographic record
Abstract
lymphocyte count (ALC) (Figure 1A), with linear and nonlinear effects on EM (Figure 1B).Model accuracy estimate was 89%, with 65% sensitivity and 94% specificity.R-IPI was very good, good and poor in 27 (5%), 212 (43%) and 258 (52%) of pts (Figure 1C).The eR-IPI included the R-IPI variables and 7 additional parameters identified by XGBoost with cutoffs based on the SHAP plots (1 point each) albumin ≤ 3g/dL, BMI < 20, creatinine ≥ 1.5g/dL, hemoglobin < 10g/dL, CIRS-G score ≥ 3, ALC < 1000/ µL, marrow involvement.The eR-IPI classified pts into 5 groups with statistically significant OS differences (Figure 1D):Very good (0 p, n = 20, 4%):1y-OS 100%, 5y-OS 79%;Good (1-2 p, n = 133, 27%):1y-OS 90%, 5y-OS 70%, Intermediate (3-4 p, n = 178, 36%):1y-OS 81%, 5y-OS 56%, Poor (5-6 p, n = 113, 18%):1y-OS 55%, 5y-OS 36%, Very poor (> 6 p, n = 53, 11%):1y-OS 28%, 5y-OS 17%.Conclusions: Our machine learning modeling identified clinical parameters associated with high-risk of EM after DLBCL diagnosis.High-risk pts cannot receive effective therapy or don't derive survival benefit from standard treatments.Trials of novel agents should consider inclusion of this underserved population.Our future analyses will focus on score refinement/validation in other DLBCL patient registries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".