Perineural Inflammation as a Novel Feature in Lichen Sclerosus: A Case Series of Histologic and Clinical Features
Bibliographic record
Abstract
ABSTRACT: Lichen sclerosus (LS) is a frequently encountered inflammatory skin disorder characterized by whitened, atrophic patches that can cause pain and pruritus. The underlying cause of this condition remains unknown. Primarily affecting the genital area, this condition carries an increased risk of developing cutaneous cancers and frequently co-occurs with autoimmune disorders. Our retrospective study aimed to explore histologic features of LS, with a particular focus on a newly established finding and its potential implications. We examined 53 histologic cases of LS collected over 2 years. Experienced pathologists evaluated and reached a consensus on the assignment of histologic features. Patient charts were manually reviewed to gather relevant demographic and clinical data. Statistical analysis was performed using IBM SPSS Statistics (2021). Of the 53 total patients identified as meeting criteria for inclusion in this study, only 8 (15%) were male. Eight cases (15%) demonstrated perineural inflammatory infiltrate. Notably, half of all samples from male patients exhibited perineural inflammatory infiltrate. A statistically significant increase ( P < 0.01) in the presence of dermal plasma cells was identified in cases with perineural inflammation versus cases without this feature. The findings of our study highlight the recurrent nature of perineural inflammation in LS, providing valuable insights into this condition. Furthermore, we observed a notable correlation between perineural inflammation, male patients, and the presence of dermal plasma cells. These discoveries contribute to a better understanding of the underlying mechanisms of LS and suggest avenues for future research into the condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".