Clinical and epidemiological features of patients with COVID-19 reinfection: a systematic review
Bibliographic record
Abstract
Recurrent positivity in a patient with COVID-19 may be due to various reasons, not necessarily reinfection. There is concern about the occurrence frequency of reinfection. Five databases and a preprint/preprint repository were searched. All case reports, case series, and observational studies were included. Bias was assessed for each study with the Newcastle-Ottawa Scale tool and reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA-2020). After eligibility, 77 studies were included for qualitative synthesis (52 case reports, 21 case series, and four case-controls; 1131 patients included). Of these, 16 studies described a second contact with the SARS-CoV-2 positive case, five studies described healthcare profession-related infection, ten studies described that the source of reinfection was likely to be from the community, one study described travel-related infection, nine studies described vulnerability-related infection due to comorbidity. The mean number of days from discharge or negative test to reinfection ranged from 23.3 to 57.6 days across the different included studies. The risk of bias for all case report/series studies was moderate/high. For observational studies, the risk of bias was low. Reinfection of patients with COVID-19 occurs between the first and second month after the first infection, but beyond, and 90 days have been proposed as a point to begin to consider it. The main factor for reinfection is contact with COVID-19 positive cases.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".