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Record W3015926352 · doi:10.1093/pch/pxaa035

A boy with multiple patches of alopecia and an affected cat

2020· article· en· W3015926352 on OpenAlexaffabout
Kam‐Lun Ellis Hon, Alexander K. C. Leung

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsLibrary scienceMedicineFamily medicinePediatrics

Abstract

fetched live from OpenAlex

A 7-year-old boy presented with itching, scaling, and areas of hair loss in the left parietal area on the scalp of 6 weeks duration (Figures 1 and 2). His past health was unremarkable. Family members were not affected. Physical examination revealed patches of fine scaling, patchy hair loss with broken hair shafts, and a black dot appearance in the left parietal area. The hair in the affected area was of irregular length. The hair-pull test was negative. [The test is conducted at more than three locations on the scalp and is used for monitoring alopecia areata, acute cases of telogen effluvium, anagen effluvium, and loose anagen syndrome. The results are considered positive if more than 10% of hairs in a pulled bundle are removed]. There was no cervical, occipital, or postauricular lymphadenopathy. The rest of the physical examination was unremarkable. A clinical diagnosis of noninflammatory tinea capitis was made based on the history of an affected cat at home and findings of multiple patches of alopecia with fine, white, adherent scaling of the scalp. The diagnosis was confirmed by potassium hydroxide wet-mount examination of scalp scrapings of the active border of an alopecic lesion, which showed septate hyphae and fungal spores. Culture of scalp wounds yielded Microsporum canis.

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)0.003
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0080.002

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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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
Published2020
Admission routes2
Has abstractyes

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