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Record W2465228716 · doi:10.7508/iej.2016.03.001

Root Canal Morphology of Permanent Mandibular Premolars in Iranian Population: A Systematic Review.

2016· article· en· W2465228716 on OpenAlexaff
Sepanta Hosseinpour, Mohammad Javad Kharazifard, Akbar Khayat, Mandana Naseri

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRoot canalDentistryMedicineOrthodonticsPopulationMandibular canineScopusMEDLINEBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: It is essential for clinicians to have knowledge about root canal configuration, although its morphology varies largely in different ethnicities and even in different individuals within the same ethnic group. The current study reviewed the root canal configuration of root canals in mandibular first and second premolars among Iranian population based on independent epidemiological studies. METHODS AND MATERIALS: A comprehensive search was conducted on retrieved articles related to root canal configuration and prevalence of each types of root canal in mandibular premolars based on Vertucci's classification. An electronic search was conducted in Medline, Scopus and Google Scholar from January 1984 to September 2015. RESULTS: In eleven studies conducted in eight provinces, 1644 mandibular first premolars and 1268 second premolars were investigated. Within mandibular first premolars, 70.9% were Vertucci's type I, followed by 10.4% type III, 7.18% type IV, 5.23% type II and 5.16% type V. In addition, among mandibular second premolars, 82.86% were type I, 6.25 type III, 5.32% type II, 4.27% type IV, and 0.69% type V. CONCLUSION: These results highlight the necessity of searching for additional possible root canals by clinicians. Moreover, these results indicated the ethnical characteristics of Iranian population regarding the morphology of mandibular premolars compared to other populations.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.296

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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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