Estimating the incidence of <scp>HIV</scp> infection in repeat blood donors with low average donation frequency
Bibliographic record
Abstract
BACKGROUND: The standard approach to estimating HIV incidence in repeat blood donors includes only donors who made two or more donations in an estimation interval. In China and some other countries, large proportions of repeat donors donate only once in a 1- or 2-year interval. The standard approach may not represent risk among all repeat donors in these areas. Two approaches to including all repeat donors in the incidence estimate were evaluated in a simulation study. STUDY DESIGN AND METHODS: Under one approach, a donor infected at the first donation contributes a partial case to incidence that equals the proportion of time since the preceding donation that is in the estimation interval. Under the other, that donor contributes a full case if at least half the time since the previous donation is in the estimation interval and nothing otherwise. Infections identified at the second or subsequent donations in the interval contribute full cases as usual. The simulations involved proportions with single donations of 11% to 65% combined with a variety of patterns of rising, falling, or constant incidence. RESULTS: The partial-case approach was unbiased under more test conditions than the whole-case approach and exhibited smaller bias when both were biased. Under both approaches, bias >10% occurred only when rates of single donations >50% were combined with large changes in incidence over time. CONCLUSION: The partial-case approach performed better than the whole-case approach. The conditions producing bias >10% are so extreme that they are unlikely to be encountered in the field.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".