Serum antibody response in COVID-19-recovered patients who retested positive
Bibliographic record
Abstract
BACKGROUND: Research studies comparing antibody response from coronavirus disease 2019 (COVID-19) cases that retested positive (RP) using reverse transcription polymerase chain reaction (RT-PCR) and those who did not retest positive (NRP) were used to investigate a possible relationship between antibody response and retesting status. METHODS: Seven data bases were searched. Research criteria included cohort and case-control studies, carried out worldwide and published before September 9, 2020, that compared the serum antibody levels of hospitalized COVID-19 cases that RP after discharge to those that did NRP. RESULTS: There is some evidence that immunoglobulin G (IgG) and immunoglobulin M (IgM) antibody levels in RP cases were lower compared with NRP cases. The hypothesis of incomplete clearance aligns with these findings. The possibility of false negative reverse transcription polymerase chain reaction (RT-PCR test results during viral clearance is also plausible, as concentration of the viral ribonucleic acid (RNA) in nasopharyngeal and fecal swabs fluctuate below the limits of RT-PCR detection during virus clearance. The probability of reinfection was less likely to be the cause of retesting positive because of the low risk of exposure where cases observed a 14 day-quarantine after discharge. CONCLUSION: More studies are needed to better explain the immune response of recovered COVID-19 cases retesting positive after discharge.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".