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Record W4391088813 · doi:10.4317/jced.61099

Evaluating Static Bone Cysts (SBCs) Through Long-Term Volumetric Analysis Using CBCT: A Study with 6-Month Follow-Up

2024· article· en· W4391088813 on OpenAlexaff
Hemant Sawhney, Mohit Pal Singh, Reshmi Banerjee, Gaurav Gupta, Ashish Kumar, Jayant Kumar Gahlot, Deepak Bhargava

Bibliographic record

VenueJournal of Clinical and Experimental Dentistry · 2024
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMedicineRadiographyAsymptomaticCone beam computed tomographyNuclear medicineRadiological weaponDentistryRadiologyOrthodonticsComputed tomographySurgery

Abstract

fetched live from OpenAlex

Background: Salivary gland bone defects are static lesions which are rare entities, generally asymptomatic and found in routine imaging exams. However, in atypical cases or when misdiagnosed, surgical intervention is carried out. Purpose: a) The study is intended to investigate the frequency of SBC's and to describe the radiological characteristics of its subtypes; b) To evaluate the efficacy of volumetric analysis tool in CBCT and; c) To describe the confirmative role of CBCT in the diagnosis of SBC's without the need for surgical intervention. Material and Methods: The study was conducted on 11 subjects with SBC detected on 3304 panoramic radiographs. CBCT images for each patient were captured at baseline and at an interval of 6 months. Radiographic sub-types of SBC based on the relationship to mandibular canal and bucco-lingual expansion were studied. Files in DICOM format were transferred into OnDemand 3D program (Cybermed Co, Seoul, Korea) and volumes in mm3 of the cavities were measured by 2 observers at both intervals. Inter and intra reliability of volumetric measurements between observers was compared using correlation coefficient and student t test. Results: >0.05). Conclusions: Stafne bone cavity, CBCT, Volumetric analysis, salivary gland, panoramic radiography.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.148
GPT teacher head0.494
Teacher spread0.345 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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