Behaviour based screening questions and potential donation loss using the “for the assessment of individualised risk” screening criteria: A Canadian perspective
Bibliographic record
Abstract
BACKGROUND: To reduce the risk of HIV transmission through transfusion, gay, bisexual and other men who have sex with men (gbMSM) are deferred from donating blood in many countries for varying lengths of time after having sex with another man. In 2021, screening algorithms to identify high-risk sexual behaviours using gender-neutral criteria (i.e., without any question on MSM or time deferral for MSM) were implemented in the United Kingdom based on recommendations in a report from the FAIR (For the Assessment of Individualised Risk) steering group. OBJECTIVES: This study examines the potential donation loss expected with these criteria if implemented in Canada. METHODS: Responses from blood donors regarding engagement in behaviours such as chemsex and anal sex with a new or multiple partners within 3 months of donation were collected using an on-site paper questionnaire. RESULTS: Applying the FAIR criteria resulted in donation loss of 1.0% (95% CI: 0.8% - 1.1%). Donation loss would be higher amongst younger donors aged 17-25 (2.0%, 95% CI: 1.6% - 2.3%). Overall, 20% of donors reported feeling uncomfortable answering study questions but only 2.0% said it would stop them from donating. CONCLUSION: Donation loss could be compensated by newly eligible gbMSM and with increased recruitment and encouraging donation from infrequent donors.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".