COVID-19, Coronavirus Vaccines, and Possible Association with Lipschütz Vulvar Ulcer: A Systematic Review
Bibliographic record
Abstract
Lipschütz genital ulcer is a self-limited, non-sexually acquired disorder characterized by the sudden onset of a few ulcers. A primary Epstein-Barr virus infection is currently considered the most recognized cause. Recent reports document cases temporally related with coronavirus disease 2019 (COVID-19) or immunization against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We carried out a review of the literature to investigate the possible association between COVID-19 or the immunization against SARS-CoV-2 and genital ulcer. The pre-registered study (CRD42023376260) was undertaken following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. Excerpta Medica, the National Library of Medicine, and Web of Sciences were searched. Inclusion criteria encompassed instances of acute Lipschütz ulcer episodes that were temporally linked to either COVID-19 or a vaccination against SARS-CoV-2. Eighteen articles were retained. They provided information on 33 patients 15 (14-24) years of age (median and interquartile range), who experienced a total of 39 episodes of Lipschütz ulcer temporally associated with COVID-19 (N = 18) or an immunization against SARS-CoV-2 (N = 21). The possible concomitant existence of an acute Epstein-Barr virus infection was excluded in 30 of the 39 episodes. The clinical presentation and the disease duration were similar in episodes temporally associated with COVID-19 and in those associated with an immunization against SARS-CoV-2. In conclusion, COVID-19 and immunization against SARS-CoV-2 add to Epstein-Barr virus as plausible triggers of Lipschütz genital ulcer.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".