More Caution Needed for Patients Recovered From COVID-19
Bibliographic record
Abstract
Since Disease Control and Prevention, 2020) These findings from recovered patients with re-detectable SARS-CoV-again. To explore this public health concern, we systematically searched PubMed (up to May 14, 2020) to summarize the available evidence from studies that documented the recovered patients with re-detectable SARS-CoV-2, using the search terms ("novel coronavirus" OR "SARS-CoV-2" OR "COVID-19") AND ("recovered" OR "discharged") AND ("positive" OR "re-detectable") with no language or time restrictions.Currently nucleic acid detection represents the most widely used test to confirm SARS-CoV-2 infection.Following had longer duration of viral shedding and even could be detected until death. (Lan et al., 2020;Zhou et al., 2020) For instance, given that the use of corticosteroids could delay the clearance of viral nucleic acids, it remains largely unknown about whether patients with severe COVID-19 and receiving corticosteroid treatment would produce transmissible SARS-CoV-2 after hospital discharge. (Ling et al., 2020)
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".