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Record W3116361612 · doi:10.4317/jced.57731

Aerosols generation using Er,Cr:YSGG laser compared to rotary instruments in conservative dentistry: A preliminary study

2020· article· en· W3116361612 on OpenAlexaff
Haitham Abdelkarim-Elafifi, C. Arnabat-Artés, I. Parada-Avendaño, Michael Jay Polonsky, Josep Arnabat‐Domínguez

Bibliographic record

VenueJournal of Clinical and Experimental Dentistry · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsInstitute of Cosmetic and Laser Surgery
Fundersnot available
KeywordsDentistryMaterials scienceLaserDental instrumentsMolarContaminationAir waterOpticsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: In restorative dentistry, the use of high-speed air turbine, which generates aerosols, can be associated with the transmission of airborne diseases. New laser technologies could be useful in reducing the amount of aerosols, but there is a lack of scientific research on this topic. MATERIAL AND METHODS: This is a descriptive study to analyze the amount of aerosols produced after class I cavity preparation using high-speed air turbine (group 1) and Er,Cr:YSGG laser with two different parameters (groups 2 and 3). Fluorescein dye was incorporated into the coolant reservoir in order to visualize the production of aerosols during each procedure. Tooth preparation was performed in a typodont with human lower molar tooth under rubber dam isolation. The procedure was carried out in a transparent plastic box to avoid aerosols dispersion. Sixteen grade I cellulose filter discs were distributed along the surfaces of the box. The area contaminated with aerosols in the filters was measured using ultraviolet illumination. RESULTS: In group 1, the contaminated surface area covered with fluorescein dye reached 77.3% (1349 cm2) of the total; in group 2 (laser with 80% water) we observed 7.3% (128 cm2) and in group 3 (laser with 40% water) it was 3.8% (68 cm2). The reduction in water parameter from 80% to 40% coincided with 48% reduction of the contaminated area on the filter discs. Focusing on the surfaces of the box, we noted that the mean contamination on the left side was more than on the right side in all three experimental groups. In group 1 using air turbine, we measured a mean of 102.6[±7.5 SD]cm2 on the left side, compared to 70.6[±32.3 SD]cm2 on the right side. In laser groups 2 and 3, a mean of 12.8[±14.9 SD]cm2 and 6.8 [±5.7SD]cm2, respectively, was described on the left surface versus 0 cm2 of surface contamination on the right surface. CONCLUSIONS: Er,Cr:YSGG laser, Aerosols, SARS-CoV-2, Rotary instruments, conservative dentistry.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.267
GPT teacher head0.489
Teacher spread0.221 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

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