Co-Occurrence of EBV-Positive Mucocutaneous Ulcer (EBV-MCU) and CLL/SLL in the Head and Neck Region
Bibliographic record
Abstract
EBV-positive mucocutaneous ulcer (EBV-MCU) was classified as a rare new entity of the lymphoproliferative B-cell diseases by the WHO in 2017 and must be distinguished from head and neck squamous cell carcinoma by early biopsy. The aim of the study is to raise awareness of the disease and to give a review of the current literature and a recommendation for EBV-MCU management. All EBV-MCU cases of the head and neck region published so far were included. We also report a case of a pharyngeal EBV-MCU in an 89-year-old patient who was immunosuppressed by chronic lymphatic leukaemia/small lymphocytic lymphoma (CLL/SLL). In contrast to all previously described cases, histopathology showed a co-infiltration of EBV-MCU and CLL/SLL. A total of 181 cases were identified on PubMed and summarised. EBV-MCU was predominantly caused by immunosuppressive drug therapy. Complete remission could be achieved in 68% of cases and was mainly attributed to a reduction of the immunosuppressive therapy alone (72%). However, some severe cases require more aggressive treatment. Regarding the various histopathologic similarities to other lymphoproliferative disorders, the diagnosis of EBV-MCU can be misleading, with a great impact on patient care and treatment. This diagnosis must be made with caution and requires a combination of clinical, morphological and immunophenotypic features.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".