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Record W2769352588 · doi:10.1159/000481308

A 12-Month-Old Healthy Girl with a New Oral Ulcer and Chronic Diaper Rash

2017· article· en· W2769352588 on OpenAlexaff
Hannah Song, Johanna S. Song, Elizabeth B. Wallace, Leonard B. Kaban, Mary Huang, Stefan Kraft, Martín C. Mihm, Daniela Kroshinsky

Bibliographic record

VenueDermatopathology · 2017
Typearticle
Languageen
FieldMedicine
TopicHistiocytic Disorders and Treatments
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicineDermatologyLangerhans cell histiocytosisRashSeborrheic dermatitisPhysical examinationEtiologyPathologyDiseaseSurgery

Abstract

fetched live from OpenAlex

A 12-month-old healthy girl presented with a chronic diaper rash. Physical examination demonstrated crusting of the scalp, erythematous papules with surrounding petechiae on the lower abdomen, and an intraoral palatal ulcer. Further imaging demonstrated bone involvement. Histopathologic examination of involved skin and the intraoral ulcer demonstrated epithelioid histiocytes with "coffee bean-shaped" nuclei, staining positive for CD1a and langerin by immunohistochemistry, consistent with Langerhans cell histiocytosis (LCH). LCH is a disease entity of unknown etiology characterized by histiocytic proliferation that most commonly presents in young children. The cutaneous findings of LCH include a seborrheic dermatitis-like and/or red-brown papular eruption. Intraoral examination is crucial as oral mucosal and maxillofacial skeletal disease can also be seen in LCH. When a child presents with a recalcitrant seborrheic dermatitis-like eruption or chronic diaper rash, the clinician should be alerted to the possibility of LCH. Timely recognition and diagnosis of LCH is important for oncologic referral, evaluation, and treatment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.289
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations3
Published2017
Admission routes1
Has abstractyes

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