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Record W3156753595 · doi:10.15331/jdsm.7192

Some Challenges and Potential Issues Regarding Cone Beam Computed Tomography of the Upper Airway

2021· article· en· W3156753595 on OpenAlexaff
Claudine Thereza-Bussolaro, Camila Pachêco‐Pereira, Manuel O. Lagravère, Carlos Flores‐Mir

Bibliographic record

VenueJournal of Dental Sleep Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCone beam computed tomographyComputed tomographyAirwayCone (formal languages)MedicineRadiologyComputer scienceSurgeryAlgorithm

Abstract

fetched live from OpenAlex

Breathing is essential for human survival. During clinical and intraoral evaluations, dentists can identify some phenotypic characteristics that have been linked to sleep-disordered breathing (SDB). Therefore, dentists could be engaged in screening and, when indicated, refer patients with high risk for obstructive sleep apnea (OSA) to appropriate medical specialists for a full diagnosis. Participation by the dentists in future interdisciplinary management can follow. Although cone-beam computed tomography (CBCT) is an established imaging modality in dentistry, it still carries some challenges related to volume rendering management and interpretation. This article outlines the challenges of assessing available CBCT and the use of commercially available rendering tools of the upper airway. The focus of this article is on important considerations regarding the use of this advanced imaging modality for nasopharyngeal obstruction assessments and volume reconstructions. It is suggested that dentists have an essential role in SDB screening, and when CBCT is already available, it could be used as a complementary tool for volumetric airway assessment. Such involvement implies the need for a more robust understanding of current CBCT imaging limitations and potential. Finally, the role of three-dimensional printing from upper airway three-dimensional imaging for educational purposes in dentistry is discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.292

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 studies0.0000.000
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 head0.017
GPT teacher head0.283
Teacher spread0.265 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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