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Record W2023874513 · doi:10.2174/1874210601307010016

Periodontal Bone Regeneration and the Er,Cr:YSGG Laser: A Case Report

2013· article· en· W2023874513 on OpenAlexaff
Douglas N. Dederich Cert Perio

Bibliographic record

VenueThe Open Dentistry Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDentistryRegeneration (biology)MedicineLaserOrthodonticsOpticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Traditional methods of regenerating bone in periodontal bone defects have been partially successful and have involved numerous protocols and materials. More recently, it has been proposed that Er,Cr:YSGG laser energy may also be beneficial in the treatment of periodontal pockets, particularly in the regeneration of bone lost due to periodontal disease. CASE DESCRIPTION: The purpose of this paper is to present a case report of the Er,Cr:YSGG laser being used to conservatively treat a recalcitrant periodontal pocket in the presence of a periodontal bone defect and that resulted in successful resolution of the pocket and significant radiographic bone fill at the 1 year recall visit. CLINICAL IMPLICATIONS: This protocol using the Er,Cr:YSGG laser for the treatment of periodontal loss of attachment and periodontal bone loss may represent a less invasive alternative than traditional open-flap periodontal surgery or the intrasulcular use of other more penetrating laser wavelengths.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.004
Science and technology studies0.0070.005
Scholarly communication0.0030.005
Open science0.0030.004
Research integrity0.0170.007
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.316
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations14
Published2013
Admission routes1
Has abstractyes

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