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Record W3183907150 · doi:10.1097/dad.0000000000002002

CD34 Staining as a Useful Tool in Disorders of Collagen Degeneration

2021· article· en· W3183907150 on OpenAlexaff

Bibliographic record

VenueAmerican Journal of Dermatopathology · 2021
Typearticle
Languageen
FieldMedicine
TopicSkin Diseases and Diabetes
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsCD34DermisStainingImmunohistochemistryInflammationGranulomaInfiltration (HVAC)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.268
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

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