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Record W1591237651 · doi:10.21726/rsbo.v9i3.995

Analysis of the instrumentation time and cleaning between manual and rotary techniques in deciduous molars

2013· article· en· W1591237651 on OpenAlexaff
Sérgio Luiz Pinheiro, Leniana Santos Neves, José Carlos Pettorossi Imparato, Danilo Antônio Duarte, Carlos Eduardo da Silveira Bueno, Rodrigo Sanches Cunha

Bibliographic record

VenueRSBO · 2013
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInstrumentation (computer programming)MolarDeciduousDentistryMedicineComputer scienceBiology

Abstract

fetched live from OpenAlex

The rotary instrumentation provides shorter instrumentation time with greater comfort for the patient but few studies have been conducted on primary teeth. Objective: this study compared the cleaning ability and instrumentation time between manual and rotary techniques in deciduous molars. Material and methods: a total of 15 molars were selected, submitted to coronal opening and root canal filled with India ink. After 48 hours, the teeth were divided into three groups: G1 – manual instrumentation with K files, G2 – rotary system Endowave, and G3 – rotary system ProTaper. After instrumentation, the teeth were sectioned and three blinded examiners evaluated the root canal cleaning. The mode of scores of examiners was analyzed by the Kruskal-Wallis test. The instrumentation time was recorded and the results were statistically analyzed by ANOVA. Results: the ProTaper system presented shorter instrumentation time compared to manual instrumentation (p = 0.0339). Endowave system did not present statistically significant difference in the instrumentation time compared to the other groups. There were no significant differences between groups concerning the ability of root canal cleaning (p = 0.6188). Conclusion: ProTaper system revealed shorter treatment time and similar cleaning ability compared to the other techniques, thus being indicated for deciduous teeth.

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 score0.024
Threshold uncertainty score0.168

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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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