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Sebaceous adenomas of the eyelid and Muir-Torre Syndrome

2015· article· en· W826566756 on OpenAlexaff
Lisa Jagan, Vasco Bravo-Filho, Mohammed F Qutub, Miguel N. Burnier

Bibliographic record

VenueBritish Journal of Ophthalmology · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEyelidMedicineOphthalmology

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: Sebaceous adenomas (SAs) are rare, benign sebaceous gland tumours of the eyelid. SAs may be associated with primary internal malignancies. This association is known as Muir-Torre Syndrome (MTS). The purpose of this study was to approximate the prevalence of SAs, to determine the reliability of the clinical diagnosis of SAs and to demonstrate immunohistochemical staining of DNA mismatch repair proteins mutL homologue 1 (MLH1) and mutS homologue 2 (MSH2) for a case of MTS. METHODS: We reviewed the histopathology reports from all eyelid specimens collected between 1993 and 2013 at the Henry C Witelson Ocular Pathology Laboratory to determine the proportion of SAs. For the SAs identified on histopathology, we looked at patient charts to see what diagnosis was originally suspected on clinical examination. Immunohistochemical staining for MLH1 and MSH2 was performed on all SAs to screen for MTS. RESULTS: Of the 5884 eyelid specimens collected, 9 were SAs (6 women, 3 men; 42-72 years old). The diagnosis of SA was suspected clinically in only one of the nine cases based on the gross appearance of the eyelid lesion. Immunohistochemistry revealed one SA case with positive MLH1 expression and negative MSH2 expression. These findings prompted systemic work-up and this patient was diagnosed with MTS after discovery of a colon adenocarcinoma T2M0N0. CONCLUSIONS: The diagnosis of eyelid SA is rare. The importance of this benign eyelid tumour stems from its association with internal malignancies in MTS. Immunohistochemical staining of mismatch repair proteins MLH1 and MSH2 is a valid and accessible strategy for investigating MTS in patients with SAs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.282
Teacher spread0.252 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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