Positive Human Immunodeficiency Virus (HIV) Test Following Influenza Vaccination: A Case Report
Bibliographic record
Abstract
False-positive human immunodeficiency virus (HIV) test results following influenza vaccination are rare today. However, with evolving vaccine formulations, unexpected cross-reactivity remains a potential concern. This case highlights the importance of recognizing this phenomenon to prevent misdiagnosis and patient anxiety. After obtaining her annual flu shot, an 82-year-old woman on hemodialysis (HD) was repeatedly discovered to have positive HIV enzyme-linked immunosorbent assay (ELISA) test results without having risk factors, and previously, the test was negative. As described in the literature, subsequent testing during the anticipated reversion period demonstrated a return to negativity, confirming a false-positive result. Cross-reactivity between HIV ELISA tests and influenza vaccines has been reported infrequently due to potential immunologic interactions. This case shows the necessity of interpreting positive HIV results with caution, especially in recently vaccinated individuals and populations undergoing frequent serological testing, such as HD patients. Clinicians should be aware of the cross-reactivity between the HIV ELISA test and the flu vaccination to recognize false-positive results, even with current influenza vaccine formulations. Awareness of this can prevent unnecessary patient distress, misdiagnosis, and unwarranted interventions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".