<scp>UK</scp> recommendations for chimerism testing and monitoring following allogeneic haematopoietic stem cell transplantation <scp>(HSCT</scp> ): Best practice consensus guidelines from the British Society for Blood and Marrow Transplant and Cellular Therapies ( <scp>BSBMTCT</scp> ), <scp>NHS</scp> England Genomic Laboratory Hub ( <scp>GLH</scp> ) Haematological Malignancies Working Group, <scp>UK</scp> Cancer Genetics Group ( <scp>UKCGG</scp> ) and the <scp>UK</scp> National External Quality Assessment Service for Leucocyte Immunophenotyping ( <scp>UK NEQAS LI</scp> )
Bibliographic record
Abstract
In allogeneic haematopoietic stem cell transplantation (HSCT), important clinical decisions depend upon assessment of chimerism, including immunosuppressant dosing and donor lymphocyte infusions (DLI), which in turn can have major impacts on disease control, graft-versus-host disease (GVHD), immunity and ultimately patient survival. There is a complex range of clinical and laboratory procedural considerations including methodology of testing, types of cell subset selection, frequency of testing, urgency of turnaround times (TATs), interplay with measurable residual disease (MRD) monitoring and duration of testing post-transplant. These aspects are routinely adapted according to disease indication, patient characteristics, donor source and intensity of transplant technique. To encourage the harmonisation of clinical and laboratory practice in the United Kingdom, we held a national workshop meeting to bring together key stakeholders to review the current literature with a view to producing a state-of-the-art position paper. Here, we present best practice consensus recommendations and identify key areas for future audit and research from the UK Cancer Genetics Group (UKCGG), NHS England Genomic Laboratory Hub (GLH) Haematological Oncology Malignancies Working Group, UK National External Quality Assessment Service for Leucocyte Immunophenotyping (UK NEQAS LI) and the British Society of Blood and Marrow Transplantation and Cellular Therapy (BSBMTCT).
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".