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Record W3082701639 · doi:10.21873/invivo.12045

Ablation Precision and Thermal Effects of a Picosecond Infrared Laser (PIRL) on Roots of Human Teeth: A Pilot Study <i>Ex Vivo</i>

2020· article· en· W3082701639 on OpenAlexaff
Reinhard E. Friedrich, Maria Quade, Nate Jowett, Peter Kroetz, Michael Amling, FELIX K. KOHLRUSCH, Jozef Zustin, Martin Gosau, Hartmut Schlüter, R. J. Dwayne Miller

Bibliographic record

VenueIn Vivo · 2020
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAblationEx vivoMaterials scienceLaserInfraredBiomedical engineeringPicosecondLaser ablationBeam (structure)DentinEr:YAG laserOral cavityDentistryOpticsIn vivoComposite materialMedicine

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: Picosecond infrared laser (PIRL) was investigated regarding its possible therapeutic application in cutting dental roots. MATERIALS AND METHODS: Extracted human teeth were processed in the root area by laser ablations followed by histological evaluation. Dentin adjacent to the cutting surface was evaluated morphometrically. RESULTS: PIRL produced clearly defined cutting boundaries in dental roots. At the bottom of the cavity, the ablation surface became slightly concave. Heat development in this scantly hydrated tissue was considerable. We attributed the excess heating effects to heat accumulation due to multiple pulse overlap across a limited scan range imposed by tooth geometries. CONCLUSION: Defined areas of the tooth root may be treated using the PIRL. For clinical translation, it would be necessary to improve beam delivery to facilitate beam steering for the intended oral application (e.g. by using a fiber) and identify optimal repetition rates/scan speeds combined with cooling techniques to minimize accumulated heat within ablation cavities.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.380

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 head0.023
GPT teacher head0.300
Teacher spread0.277 · 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 designBench or experimental
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

Citations15
Published2020
Admission routes1
Has abstractyes

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