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Record W1977094998 · doi:10.2310/7070.2002.34324

Value of Minor Salivary Gland Biopsy in Diagnosing Sjögren's Syndrome

2002· article· en· W1977094998 on OpenAlexvenueno aff
Kathrin Mahlstedt, J. Ußmüller, K. Donath

Bibliographic record

VenueThe Journal of Otolaryngology · 2002
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSialadenitisSalivary glandBiopsyMyoepithelial cellPathologySicca syndromeLymphocytic infiltrationImmunohistochemistryDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Minor salivary gland biopsy is suggested for diagnosing Sjögren's syndrome in patients with clinical signs of sicca syndrome. This biopsy is mainly examined on the basis of the semiquantitative morphologic criteria without taking into account the detection of characteristic myoepithelial sialadenitis (MESA). DESIGN: The role of minor salivary gland biopsy in the diagnosis of Sjögren's syndrome as a possible detection method for MESA was examined in a retrospective study. METHODS: Minor salivary gland biopsies were obtained from 32 patients between 1986 and 1996. Twenty-two patients fulfilled the criteria for primary and 10 patients for secondary Sjögren's syndrome based on the catalogue of the European Study Group on Diagnostic Criteria for Sjögren's Syndrome. The histopathologic assessment was based on the histomorphologic MESA criteria, that is, parenchymatrophy, interstitial lymphocytic cell infiltration, and myoepithelial cell islands. RESULTS: The histopathologic assessment revealed normal minor salivary glands in 37.5% of the cases and chronic sialadenitis in 59.4% of the patients. Only one female patient (3.1%) had changes characteristic of MESA. CONCLUSION: Minor salivary gland biospy is therefore an unsuitable method for detecting MESA if the pathomorphologic correlative of MESA is used to confirm the diagnosis of Sjögren's syndrome.

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 score0.026
Threshold uncertainty score0.594

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)0.0010.000

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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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

Citations8
Published2002
Admission routes1
Has abstractyes

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