Risk stratification for relapsed/refractory classical Hodgkin lymphoma integrating pretransplant Deauville score and residual metabolic tumor volume
Bibliographic record
Abstract
Abstract Pretransplant Deauville score (DS) is an imaging biomarker used for risk stratification in relapsed/refractory classical Hodgkin lymphoma (cHL). However, the prognostic value of residual metabolic tumor volume (rMTV) in patients with DS 4–5 has been less well characterized. We retrospectively assessed 106 patients with relapsed/refractory cHL who underwent autologous stem cell transplantation. Pretransplant DS was determined as 1–3 (59%) and 4–5 (41%), with a markedly inferior event‐free survival (EFS) in patients with DS 4–5 (hazard ratio [HR], 3.14; p = .002). High rMTV41% (rMTVhigh, ≥4.4 cm3) predicted significantly poorer EFS in patients with DS 4–5 (HR, 3.70; p = .014). In a multivariable analysis, we identified two independent factors predicting treatment failure: pretransplant DS combined with rMTV41% and disease status (primary refractory vs. relapsed). These two factors allow to stratify patients into three groups with divergent 2‐year EFS: 89% for low‐risk (51%; relapsed disease and either pretransplant DS 1–3 or DS 4–5/rMTVlow; HR 1), 65% for intermediate‐risk (28%; refractory disease and either DS 1–3 or DS 4–5/rMTVlow; HR 3.26), and 45% for high‐risk (21%; DS 4–5/rMTVhigh irrespective of disease status; HR 7.61) groups. Pretransplant DS/rMTV41% combination and disease status predict the risk of post‐transplant treatment failure and will guide risk‐stratified approaches in relapsed/refractory 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".