Validation and extension of the EBMT Risk Score for patients with chronic myeloid leukaemia (CML) receiving allogeneic haematopoietic stem cell transplants
Bibliographic record
Abstract
The European Group for Blood and Marrow Transplantation (EBMT) devised a scoring system to predict survival after allogeneic haematopoietic stem cell transplantation (HSCT) for chronic myeloid leukaemia (CML). The present International Bone Marrow Transplant Registry study of 3211 patients tested the EBMT Risk Score in a independent population, investigated the value of adding other variables, evaluated a new risk score specifically for chronic phase and compared the allograft risk scores with risk scores established by Sokal in 1984 and Hasford in 1998 for survival with non-transplant treatments. The primary outcome was 5-year survival after HSCT; survival curves, regression models and measurements of explained variation were used to compare scores. Using the EBMT scoring system, survival in the independent dataset was almost identical to those in the original EBMT publication, thus validating the EBMT Risk Score. Adding one extra variable, performance status, or designing a score specifically for early chronic phase by using the original five variables with different breakpoints gave results only slightly better than the original EBMT Score. Sokal and Hasford Scores did not predict survival after HSCT. We concluded that the EBMT Risk Score does not currently require modification.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".