The risk of allowing blood donation from men having sex with men after a temporary deferral: predictions versus reality
Bibliographic record
Abstract
BACKGROUND: In most industrialized countries, men who had sex with men (MSM) are permanently deferred from blood donation. Some countries have adopted a temporary deferral after the MSM behavior, thus avoiding donations made during the window period of human immunodeficiency virus (HIV) infection. However, one concern with this approach is the possible increase in the number of HIV-positive donations obtained from unknowingly infected, abstinent MSM; such donations might inadvertently be made available for transfusion, a risk that was previously estimated through mathematical modeling. STUDY DESIGN AND METHODS: Model predictions were compared to the actual donor rate of HIV in three countries that went from a permanent to a temporary deferral: Australia, the United Kingdom, and Canada. RESULTS: Depending on the model, a temporary deferral should have increased the rate of HIV in the male donor pool by 73% to more than 3400%. In reality, the very low baseline rate of HIV before the change in these three countries (22 cases/year) remained unchanged 2 years after the revised policy (16 cases/year). CONCLUSION: These observations strongly suggest that a temporary deferral for MSM incurs zero risk to recipients, at least in jurisdictions where HIV epidemiology is comparable to that of countries where the change happened.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".