Bibliographic record
Abstract
The novel coronavirus, SARS-cov-2, is the causal agent of Covid-19, and allegedly to have a zoonotic origin. In 2019, it was reported in Wuhan, China, and declared pandemic by World Health Organization (WHO) between 2020 and 2023 soon after, to date, there are no known effective cure or treatments. Reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) are both commonly used testing assays for SARS-cov-2 diagnosis, but only the RT-PCR recognized as the gold standard for the detection. Despite the better sensitivity and specificity, there are certain limitations RT-PCR possesses that make ELISA more preferable. This article presents a descriptive comparison of ELISA against RT-PCR in term of sensitivity and specificity. In addition, their advantages and disadvantages on these two methods were also discussed. The comparison showed better sensitivity and specificity on RT-PCR as expected, in the aspect of methodology, RT-PCR is more complex and time consuming relative to the ELISA counterpart, which making ELISA more favorable in large-scale implementations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".