CD34 Staining as a Useful Tool in Disorders of Collagen Degeneration
Bibliographic record
Abstract
ABSTRACT: The human progenitor-cell antigen CD34 is expressed in dermal dendritic cells and is lost in several disorders affecting dermal collagen. The loss of CD34 immunohistochemical staining has been demonstrated to be helpful in the histologic diagnosis of morphea, lichen sclerosus, and the classic pattern of granuloma annulare. This study characterized CD34 expression in 2 sclerosing disorders affecting the subcutis: lipodermatosclerosis (LDS) and the sclerodermoid form of chronic graft-versus-host disease (ScGVHD). In addition, we applied CD34 staining to the interstitial pattern of granuloma annulare (IGA), which is a diagnostically challenging entity with subtle amounts of dermal collagen degeneration. Fifteen cases of LDS, 6 cases of ScGVHD, and 4 cases of IGA were identified and stained with CD34. All cases of LDS showed loss of CD34 within subcutaneous septa, and 9 cases (60%) also exhibited full-thickness dermal loss of interstitial staining. All 6 cases of ScGVHD showed varying degrees of CD34 loss within the dermis and/or subcutaneous septa. The normal subcutis showed diffuse septal staining with CD34, with a density equal to that seen in the dermis. CD34 staining was lost in areas of dermal inflammation in half of the IGA cases. We conclude that CD34 staining is a useful ancillary test in disease processes affecting the subcutaneous collagen such as LDS and ScGVHD. Its utility also extends to diagnostically challenging disorders of dermal collagen degeneration such as IGA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".