Continuous false positive results by SARS-CoV-2 rapid antigen testing: a case report
Bibliographic record
Abstract
Efficient and rapid identification of active SARS-CoV-2 infections has been key to monitoring and mitigating the spread of the virus. The implementation of nucleic acid testing (e.g., RT-PCR) was broadly adopted by most public health organizations at the national and community levels across the globe, which was followed by more accessible means of home testing including lateral flow immunochromatographic assay (LFA), also known as a rapid antigen test. Here we report the case of an adult female who repeatedly and consecutively tested positive by RAT (BTNX inc). This sustained false positive was not linked with an active SARS-CoV-2 infection, which was ruled out by RT-PCR and serological analyses. SARS-CoV-2 serology revealed no detectable levels of antibodies against the nucleocapsid suggesting no recent prior infection by SARS-CoV-2. This continuous false positive was limited to BTNX testing devices. This case report aims to describe that such continuous false positives can occur and describes alternative testing approaches that can be performed to confirm RAT results. In addition, broader awareness of such occurrences is warranted in the healthcare and public health community to avoid unnecessary negative impacts on individual's day to day life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| 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".