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Record W2151908760 · doi:10.1186/1710-1492-8-10

Goldenhar syndrome: a cause of secondary immunodeficiency?

2012· article· en· W2151908760 on OpenAlexvenueno aff
S Golovine, Shu-Ya Wu, Jill V. Hunter, William T. Shearer

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
FundersNational Institutes of HealthTexas Children's Hospital
KeywordsMedicineOtitisGoldenhar syndromeMiddle earSinusitisMeningitisChronic sinusitisImmunodeficiencyPneumoniaPathologyImmunologyPediatricsSurgeryImmune systemInternal medicine

Abstract

fetched live from OpenAlex

Goldenhar syndrome (GS) results from an aberrant development of the 1st and 2nd branchial arches. There is a wide range of clinical manifestations, the most common being microtia, hemifacial microsomia, epibulbar dermoids and vertebral malformations. We present two cases of GS and secondary immunodeficiency due to anatomical defects characteristic of this disorder. Case 1 (3-year-old female) averaged 6 episodes of sinusitis and otitis media per year. Case 2 (7-year-old female) also had recurrent otitis media, an episode of bacterial pneumonia, and 2 episodes of bacterial meningitis. Their immune evaluation included a complete blood count with differential, serum immunoglobulin levels and specific antibody concentrations, lymphocyte phenotyping, and mitogen and antigen responses, the results of which were all within normal ranges. Both children demonstrated major structural abnormalities of the inner and middle ear structures, retention of fluid in mastoid air cells, and chronic sinusitis by computed tomography. These two cases illustrate how a genetically-associated deviation of the middle ear cleft can cause recurrent infections and chronic inflammation of the middle ear and adjacent sinuses, even meninges, leading to a greatly reduced quality of life for the child and parents.

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 score0.473
Threshold uncertainty score0.551

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 head0.013
GPT teacher head0.292
Teacher spread0.279 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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