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Record W4389849362 · doi:10.1016/j.jcjo.2023.11.015

Extraocular muscle enlargement in dysthyroid optic neuropathy

2023· article· en· W4389849362 on OpenAlexvenueno aff
Khizar Rana, Devanshu Garg, Lee Shien S. Yong, Carmelo Macri, Jessica Y. Tong, Sandy Patel, James Slattery, Weng Onn Chan, Garry Davis, Dinesh Selva

Bibliographic record

VenueCanadian Journal of Ophthalmology · 2023
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsExtraocular musclesOptic neuropathyMedicineOphthalmologyOptic nerveAnatomy

Abstract

fetched live from OpenAlex

ObjectiveTo investigate extraocular muscle volumes in thyroid eye disease (TED) patients with and without dysthyroid optic neuropathy (DON).DesignRetrospective cohort study.ParticipantsTED patients who had computed tomography of the orbits.MethodsThe extraocular muscles were manually segmented in consecutive axial and coronal slices, and the volume was calculated by summing the areas in each slice and multiplying by the slice thickness. Data were collected on patient demographics, disease presentation, thyroid function tests, and antibody levels.ResultsImaging from 200 orbits was evaluated. The medial rectus, lateral rectus, superior muscle group, inferior rectus, and superior oblique volumes were significantly greater in orbits with DON compared with TED orbits without DON (p < 0.01 for all). There was no significant difference in the inferior oblique muscle volume (p = 0.19). Increase in volume of the superior oblique muscle showed the highest odds for DON. Each 100 m3 increase in superior oblique, lateral rectus, inferior rectus, medial rectus, and superior muscle group volume was associated with 1.58, 1.25, 1.20, 1.16, and 1.14 times increased odds of DON.ConclusionAll extraocular muscle volumes except for the inferior oblique were significantly greater in DON patients. Superior oblique enlargement was associated with the highest odds of DON, suggesting superior oblique enlargement to be a novel marker of DON.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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.

Study designCase report
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
Published2023
Admission routes1
Has abstractyes

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