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Record W33438852 · doi:10.1177/000348941312200301

Dynamic Magnetic Resonance Imaging of the Pharynx during Deglutition

2013· article· en· W33438852 on OpenAlexfundno aff
Milan R. Amin, Stratos Achlatis, Cathy L. Lazarus, Ryan C. Branski, Pippa Storey, Bidyut Praminik, Yixin Fang, Daniel K. Sodickson

Bibliographic record

VenueAnnals of Otology Rhinology & Laryngology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institutes of HealthSchool of Medicine, New York UniversityYork University
KeywordsPharynxMagnetic resonance imagingSwallowingMedicineDynamic contrast-enhanced MRIRadiologyAnatomyNuclear medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We utilized dynamic magnetic resonance imaging to visualize the pharynx and upper esophageal segment in normal, healthy subjects. METHODS: A 3-T scanner with a 4-channel head coil and a dual-channel neck coil was used to obtain high-speed magnetic resonance images of subjects who were swallowing liquids and pudding. Ninety sequential images were acquired with a temporal resolution of 113 ms. Imaging was performed in axial planes at the levels of the oropharynx and the pharyngoesophageal segment. The images were then analyzed for variables related to alterations in the area of the pharynx and pharyngoesophageal segment during swallowing, as well as temporal measures related to these structures. RESULTS: All subjects tolerated the study protocol without complaint. Changes in the area of the pharyngeal wall lumen and temporal measurements were consistent within and between subjects. The inter-rater and intra-rater reliabilities for the measurement tool were excellent. CONCLUSIONS: Dynamic magnetic resonance imaging of the swallow sequence is both feasible and reliable and may eventually complement currently used diagnostic methods, as it adds substantive information.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.367
Teacher spread0.336 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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