Changing the deferral for men who have sex with men – an improved model to estimate HIV residual risk
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Eight published studies modelled the impact of changing from a lifetime to time-limited deferral for men who have sex with men (MSM); each predicted greater risk impact than has been observed. This study uses these previous efforts to develop an 'optimized' model to inform future changes to MSM deferrals. MATERIALS AND METHODS: HIV residual risk was calculated using observed HIV incidence/prevalence prior to the change in MSM deferral, then with the additional MSM expected under a 12-month deferral for five compliance scenarios, and finally using data observed after implementation of the deferral. Monte Carlo simulation calculated 95% confidence intervals (CI). RESULTS: The architecture of reviewed models was sound, and two were selected for combination into the optimized model. HIV risk estimated by this in the UK under MSM lifetime deferral was 0·102 (95% CI: 0·050-0·172) per million. The model predicted from a 27·8% decrease to a 47·6% increase depending upon compliance pre-implementation of the 12-month deferral. A decrease of 0·9% was observed post-implementation. For Canada, HIV risk under a 5-year deferral was 0·050 (95% CI: 0·00003-0·122) per million. Pre-implementation of the 12-month deferral, the model predicted from 30·2% decrease to 10-fold increase. A decrease of 47·0% was observed after implementation. CONCLUSION: The optimized model predicted HIV risk under 12-month MSM deferral in UK and Canada would remain low, and this was confirmed post-implementation. While the model is adaptable to other deferral scenarios, improved data quality would improve precision, particularly estimates of incidence in individuals likely to donate.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".