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Netherton Syndrome Perspectives

2025· article· en· W4411612725 on OpenAlexaff
Kam‐Lun Ellis Hon, Yuen-Ting Cheung, Z. Chan, Yeuk Ki Law, A.C. Chu, Alexander K. C. Leung, Nisha Suyien Chandran, Kin Fon Leong

Bibliographic record

VenueCurrent Pediatric Reviews · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineDermatologyPathophysiologyPsychological interventionIntensive care medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Netherton syndrome (NS), also known as Comèl-Netherton syndrome, is a rare disorder of cornification resulting from pathogenic variants in the Kazal type 5 (SPINK5) gene encoding serine protease inhibitor LEKTI. NS is characterized by the triad of congenital ichthyosiform erythroderma (CIE)/ichthyosis linearis circumflexa (ILC), trichorrhexis invaginata (TI), and atopic diathesis. Due to the severity of pathogenesis and variability in clinical presentations, the management of NS poses significant therapeutic challenges, which often require a multidisciplinary approach. Current treatment modalities predominantly consist of topical interventions and supportive measures. With an improved understanding of NS pathophysiology, emerging literature has suggested novel systemic therapeutic options for NS, which include repurposed biologics like targeted inhibitors and intravenous immunoglobulins, but there remains a paucity of literature regarding their clinical outcomes. Although often cited among dermatologists and allergists, the condition is rare in Hong Kong and Singapore, and most physicians have not managed any case. This review suggests that NS may be underestimated clinically. We aim to raise awareness for clinicians in the specific clinical characteristics and pathophysiology of NS to decide tailor-made treatment plans and improve patient outcomes.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.563
Threshold uncertainty score0.485

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 head0.026
GPT teacher head0.348
Teacher spread0.321 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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