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Record W2500827488 · doi:10.1002/mus.25258

Systematic analysis of clinical deficits in unilateral hypoglossal nerve palsy

2016· article· en· W2500827488 on OpenAlexaff
Yeyao Joe Yu, Jodi Warman‐Chardon, Pierre R. Bourque

Bibliographic record

VenueMuscle & Nerve · 2016
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsChildren's Hospital of Eastern OntarioOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsTongueParesisMedicineDysphagiaSwallowingHypoglossal nerveDysarthriaPalsyAnatomyAudiologySurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: The clinical characteristics of unilateral hypoglossal neuropathy have not been systematically analyzed. METHODS: We documented subjective abnormalities of speech and swallowing, and photographed 9 specific tongue movements and positions. Objective deficits were scored independently by 2 examiners. RESULTS: Eight patients were analyzed. Some degree of dysarthria and dysphagia was noticed by 7 and 8 patients, respectively, mostly resolving within a few months. In all subjects, there was contralateral deviation of the tongue at rest and ipsilateral deviation upon forward protrusion. Furthermore, 7 of 8 patients had deficits in using the tongue to indent the ipsilateral cheek and cover the upper lip. CONCLUSIONS: Unilateral hypoglossal nerve palsy produces mostly subtle and transient patient symptoms, even when complete. Beyond the classic sign of ipsilateral deviation on protrusion, reliable signs are contralateral deviation at rest, paresis of ipsilateral movement inside the mouth, and paresis of elevation of the tongue tip. Muscle Nerve 54: 1055-1058, 2016.

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)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.088
GPT teacher head0.440
Teacher spread0.352 · 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 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
Published2016
Admission routes1
Has abstractyes

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