Treatment outcomes of childhood <i>PICALM::MLLT10</i> acute leukaemias
Bibliographic record
Abstract
The prognostic impact of PICALM::MLLT10 status in childhood leukaemia is not well described. Ten International Berlin Frankfurt Münster-affiliated study groups and the Children's Oncology Group collaborated in this multicentre retrospective study. The presence of the PICALM::MLLT10 fusion gene was confirmed by fluorescence in situ hybridization and/or RNA sequencing at participating sites. Ninety-eight children met the study criteria. T-cell acute lymphoblastic leukaemia (T-ALL) and acute myeloid leukaemia (AML) predominated 55 (56%) and 39 (40%) patients, respectively. Most patients received a chemotherapy regimen per their disease phenotype: 58% received an ALL regimen, 40% an AML regimen and 1% a hybrid regimen. Outcomes for children with PICALM::MLLT10 ALL were reasonable: 5-year event-free survival (EFS) 67% and 5-year overall survival (OS) 76%, but children with PICALM::MLLT10 AML had poor outcomes: 5-year EFS 22% and 5-year OS 26%. Haematopoietic stem cell transplant (HSCT) did not result in a significant improvement in outcomes for PICALM::MLLT10 AML: 5-year EFS 20% for those who received HSCT versus 23% for those who did not (p = 0.6) and 5-year OS 37% versus 36% (p = 0.7). In summary, this study confirms that PICALM::MLLT10 AML is associated with a dismal prognosis and patients cannot be salvaged with HSCT; exploration of novel therapeutic options is warranted.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".