THE 23‐GENE GENE EXPRESSION‐BASED ASSAY DOES NOT PREDICT INTERIM PET SCAN RESULTS AFTER ABVD IN ADVANCED STAGE CLASSICAL HODGKIN LYMPHOMA IN THE US INTERGROUP S0816 TRIAL
Bibliographic record
Abstract
The threshold previously described was trained on overall survival (OS) after ABVD.The performance of the assay at predicting PET2 and OS in RATHL was tested using this threshold, then receiver operating characteristic (ROC) analysis was used to assess if a better threshold could be found for our cohort.Results: 284 patients were analysable as GEP failed in 31 patients.Comparing baseline demographics, the high risk GEP group had an excess of elderly, male and higher disease stage pts.The GEP score was not able to predict PET2 positivity (PET2+); 16 (12.1%)high risk pts and 26 (17.1%) low risk pts were PET2+ (12.5% and 18.3% in the stage III-IV group).Performing ROC analysis using the score as a continuous variable did not suggest a different cut off would perform better (area under the curve: 43.8%).GEP risk was associated with poorer OS in univariable analysis (3 year OS 98% vs 93% for low and high risk, respectively) but this lost significance when adjusted for other baseline factors.The classifier was not prognostic for PFS in any group in RATHL (whole population, PET2-or PET2+).Conclusions: The GEP based model was not predictive of PET2 response and is not a suitable method for determining escalation of treatment.A higher proportion of patients were assigned to the high risk group in the RATHL trial and OS was higher compared with the original intergroup study, however based on these results, a refined GEP based model with validation across other platforms is required for this modality to be used as a baseline predictor of outcome in CHL.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".