Real-World Selection of Patients for Allogeneic HCT at a Single Centre: Lack of a Suitable Donor and Other Reasons for Not Proceeding
Bibliographic record
Abstract
The reasons why patients cannot proceed with HCT, including cases where no suitable donor is identified, remain poorly described. We reviewed all referrals for allogeneic HCT to our programme between 1 January 2019 and 31 December 2023. Of 880 patients referred for allogeneic HCT, 494 (61.8%) proceeded to transplant (mean 52 ± 14.8 years, 61.5% male) using HLA-matched unrelated (64.2%) or related (19.4%) donors and HLA-mismatched (13%) or haploidentical (3%) donors. Of patients that did not proceed with HCT (386, 38.2%), disease-related causes (54.2%), patient preference (15.8%), and significant patient comorbidity (11.4%) were the most common reasons. Eleven patients (2.9% of transplants that did not proceed; 1.3% of all referrals) lacked a suitable donor and had HLA phenotypes most associated with Caucasian (six patients, 55%), First Nations, Inuit or Metis (two patients, 18%), Black African, Caribbean or African American (one patient, 9%), Asian or Pacific Islander (9%), or unknown ethnicity (one patient, 9%). Very few patients were unable to proceed with transplant due to lack of a suitable donor; however, those cases are overrepresented by non-Caucasian ethnicity relative to the population.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".