Different Laboratory Diagnosis Methods of COVID-19: A Systematic Review
Bibliographic record
Abstract
: The virus causing COVID-19 disease is known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease spread rapidly and was transmitted like a contagious disease throughout China, and then it gradually spread in other parts of the world. Accordingly, the rapid and accurate detection of the SARS-CoV-2 virus plays an essential role in selecting timely treatments, saving lives, and preventing the spread of the disease. This study summarizes the methods used to identify coronavirus nucleic acid. The effectiveness of coronavirus nucleic acid detection kits by different samples and the performance of other diagnostic techniques are also addressed in this study. We searched Embase, Google Scholar, MEDLINE, Web of Science, Scopus, and PubMed databases as well as the references of all relevant articles in English published during 2019 - 2020 using keywords related to COVID-19, detection kits, and respiratory failure and proceedings from relevant conferences and congresses. The authors collected the relevant reports, and each of the authors independently reviewed the data published in different studies. The results of previous studies indicated that the diagnosis methods of the COVID-19 disease are the RT-PCR method, ELISA kits, quick tests, white blood cell count, C-reactive protein (CRP) levels, other laboratory factors and antigenic detection methods. Given the sensitivity and specificity of these methods at different periods using different samples, the disease interpretation can be performed accurately. The findings showed that proper laboratory equipment and appropriate laboratory kits are necessary for the rapid and precise identification of COVID-19.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".