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Record W2776405296 · doi:10.4103/jispcd.jispcd_516_16

Role of cone beam computed tomography in diagnosis and treatment planning in dentistry: An update

2017· review· en· W2776405296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of International Society of Preventive and Community Dentistry · 2017
Typereview
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsMcGill University
Fundersnot available
KeywordsCone beam computed tomographyRadiation treatment planningMedicineEndodonticsSpecialtyComputed tomographyProsthodonticsOrthodonticsCone beam ctMedical physicsFixed prosthodonticsDentistryRadiologyRadiation therapy

Abstract

fetched live from OpenAlex

Accurate diagnosis and treatment planning are the backbone of any medical therapy; for this reason, cone beam computed tomography (CBCT) was introduced and has been widely used. CBCT technology provides a three-dimensional image viewing, enabling exact location and extent of lesions or any anatomical region. For the very same reason, CBCT can not only be used for surgical fields but also for fields such as endodontics, prosthodontics, and orthodontics for appropriate treatment planning and effective dental care. The aim and clinical significance of this review are to update dental clinicians on the CBCT applications in each dental specialty for an appropriate diagnosis and more predictable treatment.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.059
GPT teacher head0.374
Teacher spread0.316 · 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