How should we diagnose and treat blastic plasmacytoid dendritic cell neoplasm patients?
Bibliographic record
Abstract
Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive leukemia for which we developed a nationwide network to collect data from new cases diagnosed in France. In a retrospective, observational study of 86 patients (2000-2013), we described clinical and biological data focusing on morphologies and immunophenotype. We found expression of markers associated with plasmacytoid dendritic cell origin (HLA-DRhigh, CD303+, CD304+, and cTCL1+) plus CD4 and CD56 and frequent expression of isolated markers from the myeloid, B-, and T-lymphoid lineages, whereas specific markers (myeloperoxidase, CD14, cCD3, CD19, and cCD22) were not expressed. Fifty-one percent of cytogenetic abnormalities impact chromosomes 13, 12, 9, and 15. Myelemia was associated with an adverse prognosis. We categorized chemotherapeutic regimens into 5 groups: acute myeloid leukemia (AML)-like, acute lymphoid leukemia (ALL)-like, lymphoma (cyclophosphamide, doxorubicin, vincristine, and prednisone [CHOP])-like, high-dose methotrexate with asparaginase (Aspa-MTX) chemotherapies, and not otherwise specified (NOS) treatments. Thirty patients received allogeneic hematopoietic cell transplantation (allo-HCT), and 4 patients received autologous hematopoietic cell transplantation. There was no difference in survival between patients receiving AML-like, ALL-like, or Aspa-MTX regimens; survival was longer in patients who received AML-like, ALL-like, or Aspa-MTX regimens than in those who received CHOP-like regimens or NOS. Eleven patients are in persistent complete remission after allo-HCT with a median survival of 49 months vs 8 for other patients. Our series confirms a high response rate with a lower toxicity profile with the Aspa-MTX regimen, offering the best chance of access to hematopoietic cell transplantation and a possible cure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".