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Record W1927692695 · doi:10.1111/etp.12046

Methods and models to study nickel–titanium instruments

2013· article· en· W1927692695 on OpenAlexfundno aff
Ya Shen, Gary S.P. Cheung

Bibliographic record

VenueEndodontic Topics · 2013
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsNickel titaniumVariety (cybernetics)Root (linguistics)Computer scienceDentistryMedicineArtificial intelligenceShape-memory alloy

Abstract

fetched live from OpenAlex

Over the past two decades, nickel–titanium ( NiTi ) instruments have become an important part of the armamentarium for root canal treatment. They are increasingly used by generalists and specialists to facilitate the cleaning and shaping of root canals. Despite the advertising claims of superiority of design for some NiTi rotary systems, few have been supported by objective well‐designed studies in the literature. A variety of experiments with different models and methods have been developed for studying the properties of NiTi instruments and how they fulfil these mechanical goals. One of the main issues is how to make a rational choice for the best study model that is relevant. This article aims to present an overview of those methods and devices that have been used in endodontic literature for studying NiTi files.

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 score0.603
Threshold uncertainty score0.755

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 science0.0000.000
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 head0.050
GPT teacher head0.349
Teacher spread0.299 · 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

Citations53
Published2013
Admission routes1
Has abstractyes

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