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Record W2597604483 · doi:10.1093/pch/pxx008

Multiple red-brown macules and papules in a toddler

2017· article· en· W2597604483 on OpenAlexaff
Riley Hicks, Joseph M. Lam

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsToddlerDermatologyMedicinePsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

A previously healthy 12-month-old Caucasian girl presented with reddish, brown papules over the torso, legs, arms and scalp since 2 months of age. Past history was only significant for atopic dermatitis, torticollis, plagiocephaly, esotropia and a recent hospitalization for cervical lymphadenitis. Her development was normal; her immunizations were up to date and she had no known allergies. Family history was significant for eczema. No one else in the family had a similar eruption. On physical examination, the patient was a well-appearing infant in no apparent distress with a normal general examination, with no hepatosplenomegaly and no regional lymphadenopathy. She had multiple erythematous and hyperpigmented, 5-mm to 7-mm, oval macules and papules over the chest, back, arms and legs with some scalp involvement (Figure 1). There was no urtication on rubbing. A biopsy revealed the diagnosis. ... The biopsy revealed perivascular and interstitial infiltrates of mast cells confirming a diagnosis of urticarial pigmentosa.

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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

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 head0.014
GPT teacher head0.246
Teacher spread0.233 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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