“Non-COVID-19” Coronavirus Diseases Not to be Misdiagnosed as COVID-19
Bibliographic record
Abstract
BACKGROUND: The COVID-19 global pandemic was caused by a novel coronavirus (SARS-CoV-2), which then became an endemic infection. COVID refers to the World Health Organization's coined acronym for coronavirus disease. CASE PRESENTATION: We have, herein, reported three cases of COVIDs that could have been misdiagnosed as COVID-19. All of these families reported previous COVID-19 infection based on selfadministered Rapid Antigen Testing (RAT) and completed a period of home isolation. In these 3 cases, one child had an RSV-associated asthma attack, one had norovirus gastritis, and another had an infection with Campylobacter and E. coli. NL63, OC43, and 229E, respectively, were found by PCR in these patients. DISCUSSION: Seven human coronaviruses cause human infectious diseases. Confusion and issues associated with coronavirus disease diagnosis by Polymerase Chain Reaction (PCR) testing and Rapid Antigen Test (RAT) may arise. Some RATs are Antigen Fluorescent Immunoassays (FIA) that target monoclonal antibodies for the detection of viral nucleocapsid protein. Others target the non-nucleocapsid proteins. False positivity is possible. False negativity is also possible if the specimen's antigen level is below the test's detection limit. RAT results usually remain positive for 6 to 7 days, but they may stay positive as long as 2 weeks. Stigmatization with the COVID-19 diagnosis may occur. The PCR test is a highly sensitive 'gold standard' for the detection of COVID-19, but it can also detect non-infectious individuals' fragmented non-infectious viral nucleic acids, and could be positive for a long period. An individual may be tested positive for a few weeks to months after the individual becomes non-infectious. CONCLUSION: The cases presented here had COVID other than COVID-19, caused by coronavirus variants other than SARS-CoV-2. Co-infections with other pathogens are present in these "Non- COVID-19" COVIDs. PCR testing of non-COVID-19 COVIDs may help in the accurate diagnosis of these ailments and respiratory co-infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".