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Record W1939081607 · doi:10.1002/mus.24785

Ultrasonography detects early laryngeal muscle atrophy in an equine neurectomy model

2015· article· en· W1939081607 on OpenAlexaff
Heather Chalmers, Jeff Caswell, Justin Perkins, David Goodwin, Laurent Viel, Norm G. Ducharme, Richard J. Piercy

Bibliographic record

VenueMuscle & Nerve · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAtrophyNeurectomyMedicineAnatomyMuscle atrophyUltrasoundUltrasonographyPathologySurgeryRadiology

Abstract

fetched live from OpenAlex

INTRODUCTION: A unilateral neurectomy model was used to study the relationship between histologic and ultrasonographic tissue characteristics during muscle atrophy over time. METHODS: This investigation was an in vivo experimental study in an equine model (n = 28). Mean pixel intensity of ultrasonographic images was measured, a muscle appearance grade was assigned weekly, and muscles were harvested from 4 to 32 weeks. Minimum fiber diameter, fiber density per unit area, percent collagen, percent fat, and fiber type profile were measured from muscle cryosections and correlated with the ultrasonographic parameters. RESULTS: A significant relationship was identified between collagen content, minimum fiber diameter, and ultrasonographic muscle appearance by as early as 8 weeks. There was no apparent association between fat content of muscle and the ultrasonographic appearance of atrophy before 28 weeks. CONCLUSIONS: Early muscle atrophy before fatty infiltration is detectable with ultrasound. The effect of muscle collagen content on echointensity may be mediated by reduced fiber diameter.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.089
GPT teacher head0.383
Teacher spread0.295 · 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 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

Citations11
Published2015
Admission routes1
Has abstractyes

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