Diagnostic accuracy of ultrasensitive heat-denatured HIV-1 p24 antigen in non-B subtypes in Kampala, Uganda
Bibliographic record
Abstract
We evaluated the accuracy of heat-denatured, amplification-boosted ultrasensitive p24 assay (Up24) compared with reverse transcriptase polymerase chain reaction (RT-PCR). We tested 394 samples from Ugandans infected with HIV-1 non-B subtypes. We compared Up24 levels (HIV-1 p24 Core Profile enzyme-linked immunosorbent assay (ELISA), NEN Life Science Products) to RNA viral loads (Amplicor HIV-1 Monitor 1.5, Roche) by linear regression, and calculated sensitivity, specificity, positive and negative predictive values. Median viral load was 4.9 log10 copies/mL (interquartile range [IQR], 2.6-5.5); 114 samples (29%) were undetectable (<400 copies/mL). Sensitivity of the Up24 assay to detect viral load ≥400 copies/mL was 69%, specificity was 67%, and positive and negative predictive values were 84% and 47%, respectively. Sensitivity of Up24 was 90%, 80%, 68%, 62% and 45% to detect viral loads of >500,000, 250,000-500,000, 100,000-250,000, 50,000-100,000 and 400-50,000 copies/mL, respectively. In conclusion, when compared with RT-PCR for patients infected with non-B subtypes, the Up24 demonstrated limited sensitivity especially at low viral loads. Moreover, the Up24 was positive in 33% of samples deemed undetectable by RT-PCR, which may limit the use of the Up24 to detect viral suppression.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".