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Record W2896424392 · doi:10.1186/s13223-018-0292-3

Hyper IgE recurrent infection syndrome in South Asia: is there a different outcome?

2018· article· en· W2896424392 on OpenAlexvenueno aff
Rajiva de Silva, Dhanushka Dasanayake, Manouri P Senanayake, Ramya Ediriweera, Savithri Dias, Chandima Karunatilleke, Karen Brocas, Fariba Tahami, Suranjith L. Seneviratne

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsImmunoglobulin EMedicineSTAT3PneumoniaImmunologyGeneAntibodyBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Hyper IgE recurrent infection syndrome (HIES) is a rare immune deficiency, characterized by recurrent staphylococcal skin and lung abscesses, pneumonia and increased IgE levels. The majority of autosomal dominant HIES (AD HIGE) is due to hypomorphic mutations in the signal transducer and the activator of transcription 3 (STAT3) gene. CASE PRESENTATION: Five patients were diagnosed with HIES, based on the clinical criteria and scoring system developed at the National Institutes of Health (NIH), USA. The STAT3 gene was sequenced and previously described mutations were identified in all five patients. We compare the clinical features of our patients with those from Asia, Europe and the US. CONCLUSION: Even though the number of patients is limited, there are some clinical differences in patients from South Asia compared to European and even East Asian patients. However, the mutations detected are located at hot spots seen in western and Asian patients with AD HIGE.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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