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Record W88596955 · doi:10.12968/denu.2014.41.1.51

Traditional and contemporary techniques for optimizing root canal irrigation

2014· review· en· W88596955 on OpenAlexfundno aff
Richard Holliday, Aws Alani

Bibliographic record

VenueDental Update · 2014
Typereview
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsnot available
FundersMcGill University
KeywordsRoot canalDebridement (dental)IrrigationSyringeDentistryComputer scienceEnvironmental scienceMedicineEngineeringMechanical engineeringAgronomy

Abstract

fetched live from OpenAlex

UNLABELLED: Canal irrigation during root canal treatment is an important component of chemo-mechanical debridement of the root canal system. Traditional syringe irrigation can be enhanced by activating the irrigant to provide superior cleaning properties. This activation can be achieved by simple modifications in current technique or by contemporary automated devices. Novel techniques are also being developed, such as the Self-adjusting File (Re-Dent-Nova, Ra'anana, Israel), Ozone (Healozone, Dental Ozone, London, UK), Photoactivated Disinfection and Ultraviolet Light Disinfection. This paper reviews the techniques available to enhance traditional syringe irrigation, contemporary irrigation devices and novel techniques, citing their evidence base, advantages and disadvantages. CLINICAL RELEVANCE: Recent advances in irrigation techniques and canal disinfection and debridement are relevant to practitioners carrying out root canal treatment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.083
GPT teacher head0.341
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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations34
Published2014
Admission routes1
Has abstractyes

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