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Record W1876159921 · doi:10.1111/aej.12038

Assessment of smear layer removal protocols in curved root canals

2013· article· en· W1876159921 on OpenAlexaff
William Yeung, Denise Pontes Raldi, Rodrigo Sanches Cunha, Isabel Mello

Bibliographic record

VenueAustralian Endodontic Journal · 2013
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSmear layerSodium hypochloriteSyringeEthylenediaminetetraacetic acidDentistryGutta-perchaMolarIrrigationMedicineReamerMaterials scienceDentinNuclear chemistryRoot canalChemistryMetallurgy

Abstract

fetched live from OpenAlex

This study sought to assess if the final rinse protocol interferes with the smear layer removal in the apical area of curved canals. Sixty-four extracted human mandibular molars with curved mesial roots were instrumented with rotary files and divided into six experimental groups for final rinse: 1EDTA (syringe irrigation with 1 mL of 17% ethylenediaminetetraacetic acid (EDTA) ), 5EDTA (syringe irrigation with 5 mL of 17% EDTA), 1EDTA-P (syringe irrigation with 1 mL of 17% EDTA + pumping with gutta-percha point), 5EDTA-P (syringe irrigation with 5 mL of 17% EDTA + pumping with gutta-percha point), 1EDTA-EA (syringe irrigation with 1 mL of 17% EDTA + EndoActivator) and 5EDTA-EA (syringe irrigation with 5 mL of 17% EDTA + EndoActivator). Final rinsing was carried out over 3 min. The specimens were split lengthwise and observed under a scanning electron microscope using a score criterion. Comparison among the groups showed statistically significant difference only between the 5EDTA-EA group and the other groups (Kruskal-Wallis and Dunn's post-hoc tests, P < 0.05). The combination of 5 mL of 17% EDTA and 3 mL of 2.5% sodium hypochlorite (NaOCl) with the EndoActivator removed smear layer from the apical area of curved root canals more effectively than the other protocols used.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.379
Teacher spread0.312 · 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.

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

Citations12
Published2013
Admission routes1
Has abstractyes

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