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Record W1968436335 · doi:10.4103/0972-0707.128049

Comparative evaluation of the canal curvature modifications after instrumentation with One Shape rotary and Wave One reciprocating files

2014· article· en· W1968436335 on OpenAlexaboutno aff
Anil Dhingra, Rohit Kochar, Satyabrat Banerjee, Punit Srivastava

Bibliographic record

VenueJournal of Conservative Dentistry · 2014
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsReciprocating motionMathematicsAdobe photoshopSignificant differenceOrthodonticsEngineering drawingComputer graphics (images)EngineeringComputer scienceSoftwareArtificial intelligenceStatisticsMedicineBearing (navigation)Operating system

Abstract

fetched live from OpenAlex

AIMS: This study compared the canal curvature modifications after instrumentation with One Shape (Micro Mega) rotary file and Wave One primary reciprocating file (Dentsply Maillefer, Ballaigues, Switzerland). MATERIALS AND METHODS: Thirty International Organization for Standardization 15, 0.02 taper, Endo Training Blocks (Dentsply Maillefer) were used. In all specimens working length (WL) was established at the reference point 0. Glide path was achieved with Path-File 1, 2 and 3 (Dentsply Maillefer) at the WL. Group 1 were shaped with One Shape file and group 2 with Wave One files. Pre and post-digital images were superimposed, processed with Corel draw Graphic Suite X5 (Corel Corporation, Ottawa, Canada), Adobe Photoshop CS3 (Adobe Systems Inc., San Jose, CA) and Solid works student Edition software (Dassault Systems Solid Works Corp, S.A., Velizy, France). RESULTS: Mean was more for Wave One compared with One Shape. One-way ANOVA and t-test showed a significant difference between One Shape and Wave One at 5% level of significance (P < 0.05). CONCLUSIONS: Canals prepared with Wave One file preserved canal shape, respected the anatomical shape of J-shaped canal and produced a continuously tapered funnel.

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 score0.026
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.097
GPT teacher head0.326
Teacher spread0.229 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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