Impact of Collection Method on Assessment of Semen HIV RNA Viral Load
Bibliographic record
Abstract
BACKGROUND: The blood HIV RNA viral load is the best-defined predictor of HIV transmission, in part due to ease of measurement and the correlation of blood and genital tract (semen or cervico-vaginal) viral load, although recent studies found semen HIV RNA concentration to be a stronger predictor of HIV transmission. There is currently no standardized method for semen collection when measuring HIV RNA concentration. Therefore, we compared two collection techniques in order to study of the impact of antiretroviral therapy on the semen viral load. METHODOLOGY/PRINCIPAL FINDINGS: Semen was collected by masturbation from HIV-infected, therapy-naïve men who have sex with men (MSM) either undiluted (Visit 1) or directly into transport medium (Visit 2). Seminal plasma was then isolated, and the HIV RNA concentration obtained with each collection technique was measured and corrected for dilution if necessary. Collection of semen directly into transport medium resulted in a median HIV RNA viral load that was 0.4 log10 higher than undiluted samples. CONCLUSIONS/SIGNIFICANCE: The method of semen collection is an important consideration when quantifying the HIV RNA viral load in this compartment.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".