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Record W4390673145 · doi:10.3390/biom14010080

Advances in Sjögren’s Syndrome Dry Eye Diagnostics: Biomarkers and Biomolecules beyond Clinical Symptoms

2024· review· en· W4390673145 on OpenAlexaff
Kevin Y. Wu, Olivia Serhan, Anne Faucher, Simon D. Tran

Bibliographic record

VenueBiomolecules · 2024
Typereview
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineModalitiesDry eyesDiagnostic testSigns and symptomsPresentation (obstetrics)Medical diagnosisIntensive care medicineDermatologyPathologyInternal medicineSurgeryOphthalmologyPediatrics

Abstract

fetched live from OpenAlex

Sjögren's syndrome dry eye (SSDE) is a subset of Sjögren's syndrome marked by dry eye symptoms that is distinct from non-Sjögren's syndrome dry eye (NSSDE). As SSDE can lead to severe complications, its early detection is imperative. However, the differentiation between SSDE and NSSDE remains challenging due to overlapping clinical manifestations. This review endeavors to give a concise overview of the classification, pathophysiology, clinical features and presentation, ocular and systemic complications, clinical diagnosis, and management of SSDE. Despite advancements, limitations in current diagnostic methods underscore the need for novel diagnostic modalities. Thus, the current review examines various diagnostic biomarkers utilized for SSDE identification, encompassing serum, salivary, and tear analyses. Recent advancements in proteomic research and exosomal biomarkers offer promising diagnostic potential. Through a comprehensive literature review spanning from 2016 to 2023, we highlight molecular insights and advanced diagnostic modalities that have the potential to enhance our understanding and diagnosis of SSDE.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.374
Teacher spread0.352 · 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 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

Citations14
Published2024
Admission routes1
Has abstractyes

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