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Record W2334555081 · doi:10.9790/0853-131146168

A Study of the Diode Laser Phototherapy for Enhancing Healing and Reduction of Microbial Count in Periodontal Pockets within a Saudi Community

2014· article· en· W2334555081 on OpenAlexaff
Mahitab Mahmoud Soliman, Sherifa Mostafa M. Sabra, Ammar Saleh Al-Shammrani, Abd El-Latif A. Sorour

Bibliographic record

VenueIOSR Journal of Dental and Medical Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsMedicineDentistryReduction (mathematics)Low level laser therapyLaserLaser therapyOptics

Abstract

fetched live from OpenAlex

This research was carried out to assess Diode Laser(DL)therapeutic effects on chronic periodontitis, by reducing pockets depth and minimizing Microbial Counts (MCs).Patients(Pts)under study were 50Pts, they had chronic pockets periodontitis of more than 5mm depth, were divided into 35Pts study group(SG) and 15Pts control group(CG).All Pts were subjected to scaling, SG received DL therapy, CG received same treatment but instead of DL therapy irrigation with normal saline.The operation period (10weeks) were divided into: phase1 (baseline) at 1st week, phase2 (treatment sessions) at 2nd, 4th, and 6th week, and phase3 (follow up) at 10th week.Clinical parameters evaluation and MCs were detected during the operation period.Index of Bleeding on Probing (BOP) had been improved greatly in SG as 96.9%, while CG 20.5%.Plaque Index (PI), and Pocket Depths (PD), were more reduced in SG than CG.Colony Forming Units/ml (CFUs/ml) were reduced with DL therapy which revealed (400, 320, 250, 170 and 90) and (410, 350, 300, 260 and 190) for SG and CG respectively, that were confirmed SG were significantly better than CG.DL irradiation revealed anti-microbial effect and reduction of inflammation in periodontal pockets, also, combination with scaling, supports healing of periodontal pockets through microbial eliminating.

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)0.002
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 score0.128
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.330
Teacher spread0.304 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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Same venueIOSR Journal of Dental and Medical SciencesSame topicLaser Applications in Dentistry and MedicineFrench-language works237,207