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Record W4405896192 · doi:10.1038/s41598-024-81958-y

0.5T MRI as a competitor to CT for sinus imaging

2024· article· en· W4405896192 on OpenAlexafffund
Mark E. Parker, Steven Beyea, James Rioux, Brian N. King, Mohamed Abdolell, Sarah Reeve, Beverly Lieuwen, Chris V. Bowen, David Volders

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsDiscovery CentreNova Scotia Health AuthorityIzaak Walton Killam Health CentreDalhousie University
FundersDalhousie University
KeywordsMagnetic resonance imagingMedicineRadiologyComputed tomographySinus (botany)Computer scienceBiologyZoology

Abstract

fetched live from OpenAlex

The goal of this study was to determine how radiologists' rating of image quality when using 0.5T Magnetic Resonance Imaging (MRI) compares to Computed Tomography (CT) for visualization of pathology and evaluation of specific anatomic regions within the paranasal sinuses. 42 patients with clinical CT scans opted to have a 0.5T MRI scan for this study. Scans were completed from June 2021 to June 2022 with an average of 65.2 days from CT to MRI. A neuroradiologist and neuroradiology fellow evaluated the images to answer several questions and provide a confidence score for each based on image quality. Responses between the CT and MRI scans were compared for intramodality and intermodality agreement. The Likert scores demonstrate that MRI performed well in assessing mucosal thickening. Performance was not adequate for anatomical questions for presurgical planning. 0.5T MRI is able to produce high quality imaging of the sinuses. This could be used as a radiation free test to correlate mucosal thickening with patient's symptoms. However, a CT would be needed to screen for ostiomeatal obstruction and anatomical visualization of critical variants for presurgical planning.

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)0.006
metaresearch head score (Gemma)0.019
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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