Poor outcome after hematopoietic stem cell transplantation of patients with unclassified inherited bone marrow failure syndromes
Bibliographic record
Abstract
Classification of inherited bone marrow failure syndromes (IBMFSs) according to clinical and genetic diagnoses enables proper adjustment of treatment. Unfortunately, 30% of patients enrolled in the Canadian Inherited Marrow Failure Registry (CIMFR) with features suggesting hereditability could not be classified with a specific syndromic diagnosis. We analyzed the outcome of hematopoietic stem cell transplantation (HSCT) in unclassified IBMFSs (uIBMFSs) and the factors associated with outcome. Twenty-two patients with uIBMFSs and 70 patients with classified IBMFSs underwent HSCT. Five-year overall survival of uIBMFS patients after HSCT was inferior to that of patients with classified IBMFSs (56% vs 76.5%). The outcome of patients with uIBMFS who received cord blood was significantly lower than that of patients who received other stem cell sources (14.8% vs 90.9%). Engraftment failure was higher among patients with uIBMFS who received cord blood than those who received bone marrow. None of the following factors were significantly associated with poor survival: transfusion load, transplant indication, the intensity of conditioning regimen, human leukocyte antigen-identical sibling/alternative donor. We suggest that identifying the genetic diagnosis is essential to modulate the transplant procedure including conditioning agents and stem cell sources for better outcome and the standard cord blood transplantation (CBT) should be avoided in uIBMFS.
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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".