The above letter was sent to Heddle et al.: Dr Heddle offered the following reply
Bibliographic record
Abstract
Hematopoietic mixed chimerism after allogeneic BMTChimerism after BMT has been documented by various genetic markers, RBC antigens, and, less frequently, by immunoglobulin allotypes.These markers are less sensitive than short tandem repeats (STRs), which provides a powerful tool for analysis of human polymorphisms.Application of STR analysis in patients undergoing allogeneic BMT has been used to detect mixed or complete chimerism, recurrent leukemia, and endogenous repopulation of marrow in early and late phases after transplant.It can detect minor populations of DNA in the period after transplant with adequate sensitivity, whereas traditional immunohematologic evaluation is limited because of the necessity of transfusing RBCs. 1 We describe a 21-year-old man with acute lymphoblastic leukemia who received an allogeneic unrelated BMT from a female donor after achieving complete remission.The patient blood group was A1/C-c + D -E-e + , and the donor's was O/C-c + D + E + e-; that is, there was a minor ABO mismatch.The marrow was treated to remove RBCs precursors.This case, as has been seen with studies on lymphocytes, 5 underlines the limitation of assaying for chimerism with a whole-blood sample compared to enriched RBCs.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.032 | 0.019 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".