Plasmacytoid dendritic cells in hypertrophic discoid lupus erythematosus: an objective evaluation of their diagnostic value
Bibliographic record
Abstract
BACKGROUND: Hypertrophic discoid lupus erythematosus (HDLE), a rare variant of lupus skin disease, is difficult to distinguish from squamous neoplasms and certain dermatoses microscopically. Recently, recognition of the pathogenetic significance of plasmacytoid dendritic cells (PDCS) in cutaneous lupus erythematosus (LE) and of their patterns of distribution in different manifestations of the disease prompted us to study their diagnostic value in the context of HDLE. METHODS: Using immunohistochemistry (CD123) to label the cells, we examined their quantities and patterns of distribution in 27 tissue samples of HDLE from nine patients compared with 39 inflammatory and neoplastic control samples from 36 patients. RESULTS: Using three parameters pertaining to PDCs: (i) their representation of 10% or more of the inflammatory infiltrate, (ii) their arrangement in clusters of 10 cells or more and (iii) their presence at the dermoepidermal junction, we found them to have significant diagnostic value, with accuracies of 77%, 74% and 71%, respectively. CONCLUSIONS: This study supports the careful descriptive observations of previous authors in the field. It also lends validity to the diagnostic step of mapping, immunohistochemically, the density and distribution of PDCs in suspected cases of HDLE.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".