Risk of transfusion‐related acute lung injury and <scp>human immunodeficiency virus</scp> associated with donations from trans donors in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Blood operator must establish selection criteria according to the populations at risk of blood-related infections and complications. Therefore, this study aimed to assess the risks of transfusion-related acute lung injury (TRALI) and human immunodeficiency virus (HIV) associated with donations from trans persons. MATERIALS AND METHODS: Donor screening data from Héma-Québec were used. The risks of TRALI and HIV were estimated based on internal data and assumptions derived from the literature. The risk was assessed under four scenarios: a most likely scenario, an optimistic scenario and two pessimistic scenarios. All scenarios assumed no prior screening for trans donors. RESULTS: The trans population comprised 134 donors, including 94 (70.1%) trans men. Of the 134 donors, 58 (43.3%) were deferred from donating a blood-derived product because of an ongoing gender-affirming genital surgery, and the remaining 76 (56.7%) were eligible donors. The risk of having a TRALI-causing donation, given that it comes from a trans man, was estimated at one every 115-999 years for all scenarios. The risk of having an HIV-contaminated donation, given that it comes from a trans woman, was estimated at one every 1881-37,600 years for all scenarios. CONCLUSION: This study suggests that donations from trans persons are associated with a negligible risk of TRALI and HIV.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".