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Record W4312879816 · doi:10.5114/ko.2022.120604

Diplopia in a patient with Satoyoshi syndrome and mitochondrial myopathy

2022· article· en· W4312879816 on OpenAlexaboutno aff
Ewelina Jagiełło-Roszkowska, Dorota Białas-Niedziela, Monika Turczyńska, Joanna Brydak-Godowska, Anna Waśkiel‐Burnat, Lidia Rudnicka, Dariusz Kęcik

Bibliographic record

VenueKlinika Oczna · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsnot available
Fundersnot available
KeywordsDiplopiaMitochondrial myopathyMyopathyMedicineInternal medicineSurgeryBiologyMitochondrial DNABiochemistry

Abstract

fetched live from OpenAlex

INtroductIoNThe diagnostic work-up in patients with ocular motility impairment requires taking a detailed history with a focus on both ophthalmic and systemic conditions.A complete ophthalmic and strabismological examination is necessary.In addition, patients with diplopia often need a referral for laboratory tests, imaging examinations, and consultations with other medical specialists.Both physicians referring patients to strabismology outpatient clinics and diplopic patients themselves may not link their ophthalmic abnormalities with systemic diseases.This case report serves to highlight that diplopia may be caused by rare disorders.The diagnostic process in diplopic patients must be oriented towards determining the cause of the condition and followed by the selection of an appropriate treatment modality.In most cases, effective treatment of the underlying disease or reducing the severity of symptoms can help the patient.The variable nature of the disorder with intermittent periods of more severe diplopia and remission usually indicate non-ophthalmic causes.The aim of this article is to highlight the complex etiology of visual disorders based on a case report.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.204
Teacher spread0.197 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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