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Record W4410134702 · doi:10.1093/pch/pxaf006

A 22-month-old girl with yellow nodules

2025· article· en· W4410134702 on OpenAlexaffabout
Gaganvir Parmar, Jonathan P. Wong, Peter Wong

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsGirlArtMedicinePsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

A 22-month-old female toddler was referred for yellow skin nodules. These lesions, which emerged gradually over the past 6 months involved the flexor creases but were also present on the ankles, elbows, shoulders and back (Figure 1). Notably, there was no joint involvement or ocular findings. ... The child was born at term in southern India following an uneventful pregnancy and delivery by caesarean section. The child migrated to Canada at 15 months of age, shortly after which her skin lesions first appeared. The child demonstrated normal growth and development, and there were no previous hospitalizations or surgeries. Apart from the noted skin lesions, the physical examination was otherwise unremarkable: a well-nourished appearing girl weighing 10.9 kg (45th percentile) and a height of 81 cm (12th percentile) with pink and well-perfused extremities; no signs of respiratory distress; a chest clear to auscultation; and no hepatosplenomegaly. The general precordial exam was unremarkable, and her blood pressure measured at 103/72 mmHg. The child’s parents hail from a similar region in southern India but are non-consanguineous. Both parents display an elevated total cholesterol and low-density lipoprotein cholesterol (LDL-C) level, and a low–high-density lipoprotein cholesterol (HDL-C) level but are otherwise healthy. Neither parent exhibited ocular nor cutaneous findings.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.295
Teacher spread0.284 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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