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Record W2157187059 · doi:10.1259/dmfr.20130022

Topical contrast agents to improve soft-tissue contrast in the upper airway using cone beam CT: a pilot study

2013· article· en· W2157187059 on OpenAlexaff
Noura Alsufyani, Michelle Noga, Warren H. Finlay, Paul W. Major

Bibliographic record

VenueDentomaxillofacial Radiology · 2013
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNasal cavityMedicineRadiodensityBarium sulfateSyringeCone beam computed tomographyRadiographyAirwayNasal LavageSinus (botany)Soft tissueCone beam ctNuclear medicineBiomedical engineeringRadiologyNoseMaterials scienceAnatomySurgeryComputed tomography

Abstract

fetched live from OpenAlex

The purpose of this study is to explore the topical use of radiographic contrast agents to enhance soft-tissue contrast on cone beam CT (CBCT) images. Different barium sulphate concentrations were first tested using an airway phantom. Different methods of barium sulphate application (nasal drops, syringe, spray and sinus wash) were then tested on four volunteers, and nebulized iodine was tested in one volunteer. CBCT images were performed and then assessed subjectively by two examiners for contrast agent uniformity and lack of streak artefact. 25.0% barium sulphate presented adequate viscosity and radiodensity. Barium sulphate administered via nasal drops and sprays showed non-uniform collection at the nostrils, along the inferior and/or middle nasal meatuses and posterior nasal choana. The syringe and sinus wash showed similar results with larger volumes collecting in the naso-oropharynx. Nebulized iodine failed to distribute into the nasal cavity and scarcely collected at the nostrils. All methods of nasal application failed to adequately reach or uniformly coat the nasal cavity beyond the inferior nasal meatuses. The key factors to consider for optimum topical radiographic contrast in the nasal airway are particle size, flow velocity and radio-opacity.

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)0.001
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 score0.074
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.034
GPT teacher head0.309
Teacher spread0.275 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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