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Record W4282934013 · doi:10.1080/2576117x.2022.2074239

Vertical One-and-a-Half Syndrome with Pseudoabducens Palsy and Midbrain Horizontal Gaze Paresis

2022· article· en· W4282934013 on OpenAlexaff
Yasser Aladdin, Bader Shirah, Khurshid Khan

Bibliographic record

VenueJournal of Binocular Vision and Ocular Motility · 2022
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMidbrainSuperior colliculusSaccadic maskingAnatomyGazeNeurosciencePsychologyEye movementMedicineCentral nervous system

Abstract

fetched live from OpenAlex

The rostral mesencephalon may influence ocular motility in the vertical, horizontal, and torsional trajectories through intricate supranuclear, internuclear, and infranuclear neural networks. Strategic unilateral midbrain lesions may result in contralateral horizontal gaze palsy with saccadic failure due to combined interruption of supranuclear corticofugal fibers from the frontal eye field and colliculofugal fibers from the superior colliculus. In this article, we report a patient who sustained combined vertical and horizontal gaze deficits after a single infarct involving the mesodiencephalic junction. The neural substrate for each deficit is briefly discussed in light of clinical findings. This case presented a triad of three distinct syndromes of horizontal gaze paresis, vertical one-and-a-half syndrome, and pseudoabducens palsy due to damage of nuclear and supranuclear projections within the rostral mesencephalon. This combination was due to a single embolic infarct in the territory of the posterior thalamosubthalamic artery (artery of Percheron) that arises at the basilar bifurcation. Coexistence of these phenomena exemplified how rostral midbrain lesions may affect ocular motility in the vertical, horizontal, and torsional planes, along with disruption of normal vergence control.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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