High level of HIV‐2 false positivity in KwaZulu‐Natal province: A region of South Africa with a very high HIV‐1 subtype C prevalence
Bibliographic record
Abstract
Human immunodeficiency virus 2 (HIV-2) is found predominantly in West Africa. It is not unlikely, however, that HIV-2 may also be found in South Africa, due to the influx of immigrants into this country. It is important to distinguish between HIV-1 and HIV-2 since the clinical courses and treatment responses of these viruses are different. Routine serological methods for diagnosing HIV do not differentiate between HIV-1 and -2 infections, while rapid tests, viral load quantification and PCR are HIV-type--specific. The objective of this study was to describe the seroprevalence and molecular epidemiology of HIV-2 in KwaZulu-Natal, one of the regions with the highest HIV prevalence in the world and home of the two largest harbors in South Africa. HIV-1 positive samples were screened for antibodies against HIV-2, using a rapid test. The confirmation of HIV-2 positive samples was done by PCR. Of the 2,123 samples screened, 319 (15%) were identified as positive by the rapid test. None of these samples were confirmed positive by PCR. To explore this discrepancy in the results, a subset (n = 52) of the rapid HIV-2 positive samples was subjected to Western blotting. Thirty-seven (71%) of these were positive, yielding an overall HIV-2 seroprevalence of 10.6%. Three out of 28 (10.7%) Western blot positive samples were positive by a Pepti-LAV assay. This discrepancy between serological and molecular confirmation may be attributed to non-specific or cross-reacting antibodies. The use of rapid tests and Western blots for HIV-2 diagnosis in South Africa should be interpreted with caution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".