Donor–Recipient Story in Allogeneic Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
Patients with a variety of blood, immune, and metabolic disorders may require an allogeneic hematopoietic stem cell transplant as part of their treatment. However, over 70% of these patients do not have a matched sibling donor and require an alternative donor, such as a matched unrelated donor. We present a multi-part story of a Canadian stem cell recipient who underwent transplantation for treatment of refractory chronic myelogenous leukemia, and the matched unrelated donor who saved his life. The story segments feature excerpts from interviews with the donor and the recipient, along with representative images of both storytellers. The excerpts were optimized for publication on social media and were arranged to build a story arc that parallels the journey of the donor and recipient together. This donor-recipient story may serve as a resource to help raise awareness about stem cell donation and to encourage eligible individuals to register as donors. The story is one of several developed by Why We Swab, a library of stories in stem cell donation in Canada (Facebook, Twitter, and Instagram; @WhyWeSwab) to support the recruitment of committed unrelated donors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".