Exploring Cutaneous Lymphoproliferative Disorders in the Wake of COVID-19 Vaccination
Bibliographic record
Abstract
Background: Individual reports have described lymphoproliferative disorders (LPDs) and cutaneous lymphomas emerging after administration of the COVID-19 vaccine; however, the relationship between reactions and vaccine types has not yet been examined. Objective: Determine if there are cases of cutaneous LPDs associated with certain COVID-19 vaccines and their outcomes. Methods: We analysed PubMed, the Vaccine Adverse Events Reporting System (VAERS), and our database for instances of biopsy-proven LPDs following COVID-19 vaccines. Results: Fifty cases of biopsy-proven LPDs arising after COVID-19 vaccination were found: 37 from medical literature, 11 from VAERS and two from our institution. Geographical distribution revealed the most cases in the United States, Italy, and Greece, with single cases in Spain, Colombia, Canada, Japan, and Romania. The average age of patients was 53; with a slight male predominance (male-to-female ratio of 1.5:1). The Pfizer-BioNTech vaccine was associated with LPDs in 36/50 (72%) cases, aligning with its 70% share of the global vaccine market. Histopathology revealed CD30+ in 80% of cases. The most prevalent form of LPD was lymphomatoid papulosis (LyP, 30%). All reported cases produced favourable outcomes (either complete or near-complete remission). Therapeutic approaches ranged from observation to treatment with steroids, methotrexate, or excision. Conclusion: LPDs after COVID-19 vaccination appear in the context of the same vaccines (proportionally to their global market shares), share clinical and pathological findings, and have indolent, self-limited character.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".