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Record W2044769193 · doi:10.1111/ocr.12064

Effect of low‐level laser therapy on dental root cementum remodeling in rats

2015· article· en· W2044769193 on OpenAlexaff
Mahmoud A Alsulaimani, Michael R. Doschak, Douglas N. Dederich, Carlos Flores‐Mir

Bibliographic record

VenueOrthodontics and Craniofacial Research · 2015
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsWorkers Compensation Board of AlbertaUniversity of Alberta
Fundersnot available
KeywordsCementumDental cementumDentistryIn vivoMedicineSignificant differenceConfocal laser scanning microscopyLow level laser therapyLaser therapyDentinBiomedical engineeringLaserInternal medicineBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the amount of the cementum layer formed over the rat's dental root surfaces by daily application of low-level laser therapy (LLLT) for 2 weeks. METHODS: Twelve female Sprague-Dawley (SD) rats were divided into two groups: six rats received daily LLLT (Ga-Al-As, 830 nm), and six rats received no treatment (control). The treatment lasted 2 weeks. In vivo Micro-CT imaging analyzed the root's hard tissue volumetric changes. The cementum thickness was evaluated histologically. RESULTS: Total cementum thicknesses in the LLLT group increased significantly (p = 0.015) compared to the control group. This significant increase in the cementum thickness, verified histologically, was not detectable during in vivo Micro-CT imaging, which showed no significant difference between the groups regarding the root hard tissues volumetric changes over the 2-week evaluation period. CONCLUSION: Two weeks of daily application of LLLT significantly increased rat's dental root cementum thickness as determined histologically. However, in vivo Micro-CT imaging failed to accurately reveal this cementum growth as it was not possible to differentiate dentinal changes.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.173
GPT teacher head0.455
Teacher spread0.282 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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