Models used to predict the impact of having less stringent deferral policies for men who had sex with men: can we validate these predictions?
Bibliographic record
Abstract
Many jurisdictions still do not accept blood donations from men who had sex with men ( MSM ). Those who defend the status quo argue that a less stringent policy would unduly increase the risk to recipients. In an effort to address this dilemma, investigators have tried to project the impact of having less stringent deferral policies on HIV transmission risk, using mathematical models that rely on empirical data. Under certain assumptions, these models predicted very small but definite increases in risk if MSM were allowed to donate after a temporary deferral period. However, the predicted increase in the number of transfusion‐transmitted infections would be so small as to remain imperceptible in reality. Models also predict a sizeable increase in the number of HIV ‐positive donors who would present to donate, an outcome that should be more readily observable. When applied to the Australian experience, where a one‐year deferral policy for MSM was implemented, most models would have predicted significant increases in the prevalence of HIV in male donors. The actual rate of HIV among Australian male donors remained very low and unchanged, suggesting that these models were overly pessimistic. It will be interesting to validate this finding in other countries that implemented a time‐based deferral. The Australian experience, if confirmed in other countries, would suggest that a time‐based deferral for MSM poses an even lower risk than what the models predict.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".