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Record W3026454188 · doi:10.3390/app10103513

Digital Design of Minimally Invasive Endodontic Access Cavity

2020· article· en· W3026454188 on OpenAlexaboutno aff
Gianluca Gambarini, Massimo Galli, Antonio Morese, Fouad Abduljabbar, Marco Seracchiani, Luigi Vito Stefanelli, Massimo Giovarruscio, Dario Di Nardo, Luca Testarelli

Bibliographic record

VenueApplied Sciences · 2020
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDentinCone beam computed tomographyOrthodonticsDentistryComputer scienceComputed tomographyMedicineSurgery

Abstract

fetched live from OpenAlex

New minimally invasive endodontic cavities have been described and proposed to preserve dentin (and enamel) through strategic access, including point endodontic access cavity (PEAC). There is no consensus to what extent PEAC contributes to tooth’s resistance to fracture, because there is no agreement on how PEAC should be performed. The purpose of the present study is to describe and classify four different types of PEACs and to examine if a dynamic navigation system /DNS) could allow planning and precisely executing these cavities in vitro. Forty TrueTooth TM Replica # 3-001 models, were randomly divided into four identical groups of ten and scanned using a cone bean computed tomography (OP-Maxio 300, Instrumentarium-Kavo, Finland). Then, four different access cavities were planned and performed by using DNS (Navident dynamic navigation system, ClaroNav, Toronto, ON, Canada). For each tooth, a different PEAC was designed to obtain endodontic access to the main mesio-buccal canal (MB1), resulting in a different location of the entry point on the occlusal surface of the tooth. Precision was evaluated by comparing deviation in the inclinations between the planned and real cavity. Data were recorded and statistically analyzed. DNS allowed preparation of minimally invasive “straight line” cavities, with some differences in the accuracy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.088
GPT teacher head0.306
Teacher spread0.217 · 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 designBench or experimental
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

Citations15
Published2020
Admission routes1
Has abstractyes

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