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Record W4316039092 · doi:10.3390/ijms24021580

An Overview of the Dry Eye Disease in Sjögren’s Syndrome Using Our Current Molecular Understanding

2023· review· en· W4316039092 on OpenAlexaff
Kevin Y. Wu, Merve Kulbay, Cristina Tănăsescu, Belinda Jiao, Bich Hong Nguyen, Simon D. Tran

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsMcGill UniversityCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsCurrent (fluid)DiseaseMedicineDermatologyPathologyEngineering

Abstract

fetched live from OpenAlex

Sjögren's syndrome is a chronic and insidious auto-immune disease characterized by lymphocyte infiltration of exocrine glands. The patients typically present with ocular surface diseases related to dry eye and other systemic manifestations. However, due to the high prevalence of dry eye disease and the lack of objective and clinically reliable diagnostic tools, discriminating Sjögren's syndrome dry eye (SSDE) from non-Sjögren's syndrome dry eye (NSSDE) remains a challenge for clinicians. Diagnosing SS is important to improve the quality of life of patients through timely referral for systemic workups, as SS is associated with serious systemic complications such as lymphoma and other autoimmune diseases. The purpose of this article is to describe the current molecular understanding of Sjögren's syndrome and its implications for novel diagnostic modalities on the horizon. A literature review of the pre-clinical and clinical studies published between 2016 and 2022 was conducted. The SSDE pathophysiology and immunology pathways have become better understood in recent years. Novel diagnostic modalities, such as tear and saliva proteomics as well as exosomal biomarkers, provide hope on the horizon.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.676
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.280
GPT teacher head0.472
Teacher spread0.192 · 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 designSystematic review
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

Citations39
Published2023
Admission routes1
Has abstractyes

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