Audit of donor centre: guidelines by the World Marrow Donor Association Quality and Regulation Working Group
Bibliographic record
Abstract
According to the Standards of the World Marrow Donor Association (WMDA) 2020 [1] unrelated stem cell donor registries are responsible for compliance of their donor centres with these Standards. To ensure high stem cell product quality and high standards for safety and satisfaction of voluntary unrelated stem cell donors, we present here guidelines for audits of donor centres (DC) that can be used by new and established donor registries. They have been developed for registries relying on independent national or international DCs for the recruitment and management of Unrelated Donors (UD) for verification typing (VT)/extended tying (ET), work up processes and Hemopoietic Progenitor Cell (HPC) donation. The main goal of these guidelines is to support registries in verifying and auditing their affiliated DCs to ensure they are compliant with the WMDA Standards, as well as WMDA recommendations. We define the general requirements and recommendations for collaboration with the DC and guidelines to manage the UD, step by step from recruitment to follow-up. We also provide a checklist, intended to serve as a resource for auditors performing an audit at a DC.
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 | 0.116 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".