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Cone Beam CT Features and Oral Radiologist’s Decision-making ofArrested Pneumatization of the Sphenoid Sinus

2022· article· en· W4310570700 on OpenAlexaff
Noura Alsufyani, Nouf Alsuayri, Raghad Fahad Alrasheed

Bibliographic record

VenueCurrent Medical Imaging Formerly Current Medical Imaging Reviews · 2022
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSkullMedicineCone beam computed tomographyRadiographyConfidence intervalSinus (botany)RadiologyDentistryNuclear medicineComputed tomographyAnatomyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the demographic and radiographic features of arrested pneumatization of the sphenoid sinus (APS) and their influence on the confidence of oral and maxillofacial radiologists (OMFRs) in diagnosing APS. METHODS: Reports of cone beam computed tomography (CBCT) APS were retrieved, and the demographic and radiographic features were retrospectively analyzed. Five OMFRs assessed the CBCT images and their confidence in diagnosing APS. The OMFRs' experience (years), expertise (skull-base CBCT cases/month) and diagnostic confidence level were analyzed for agreement and associations with demographic or radiographic features. RESULTS: Of 29 APS cases, 17 (58.6%) were females, and the mean age was 29.9±19 years. Twenty cases (69.0%) presented unilaterally, and 27 (93.1%) involved the sphenoid body. The most common accessory site was the pterygoid process (19, 65.5%). The vidian canal and foramen rotundum were involved in 27 (93.1%) and 17 (58.6%) cases, respectively. Most cases (28, 96.6%) were well-defined, corticated, and showed mixed attenuation. APS diagnostic confidence was higher among the expert OMFRs (72.4%-82.8% vs. 58.6%-62.1%). CONCLUSION: Radiographic features differentiating APS from skull-base tumors were shown on CBCT. The confidence of OMFRs with similar experience in years depended on their frequency of examining CBCT cases involving the skull base.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.352
Teacher spread0.326 · 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 designOther design
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

Citations0
Published2022
Admission routes1
Has abstractyes

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