Quantitative estimate of the risks and benefits of possible alternative blood donor deferral strategies for men who have had sex with men
Bibliographic record
Abstract
BACKGROUND: Implementation of sensitive screening methods for human immunodeficiency virus (HIV) and hepatitis viruses prompts the question of what quantitative risks may result from altered deferral strategies for donation of blood by men who have had sex with men (MSM). STUDY DESIGN AND METHODS: Quantitative probabilistic models were developed to assess changes in the residual risk of transfusion-transmitted HIV and hepatitis B virus (HBV) associated with blood testing and quarantine release errors (QREs) in the initial year of two hypothetical policy scenarios that would allow donations from donors who have abstained from MSM behavior for at least 5 years (MSM5) or at least 1 year (MSM1). RESULTS: The MSM5 and MSM1 models, respectively, predicted annual increases in units of HIV-infected blood of 0.5% (0.03 mean additional units; 95% confidence interval [CI], 0-1) and 3.0% (0.18 mean additional units; 95% CI, 0-1) over current estimated HIV residual risk using recent, nationwide biologic product deviation reports to estimate QRE rates. These estimates are approximately 10-fold lower than estimates based on New York State QRE data from the previous decade. The models predicted smaller increases in infectious HBV donations. CONCLUSIONS: QREs remain the most significant preventable source of risk. More accurate inputs, including the percentage of MSM in the population, the percentage of MSM who have abstained from MSM activity for 1 or 5 years, the prevalence of HIV and HBV in MSM who have abstained from MSM activity for 1 or 5 years, the rate of self-deferral, and QRE rates, are required before making more precise predictions.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".