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Record W2964144491 · doi:10.1155/2019/9187978

Antioxidants as Adjuvants in Periodontitis Treatment: A Systematic Review and Meta-Analysis

2019· review· en· W2964144491 on OpenAlexafffund
Micaele Maria Lopes Castro, Nathallia Neves Duarte, Priscila Cunha Nascimento, Marcela Baraúna Magno, Nathália Carolina Fernandes Fagundes, Carlos Flores‐Mir, Marta Chagas Monteiro, Cassiano Kuchenbecker Rõsing, Lucianne Cople Maia, Rafael Rodrigues Lima

Bibliographic record

VenueOxidative Medicine and Cellular Longevity · 2019
Typereview
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversity of Alberta
FundersPró-Reitoria de Pesquisa e Pós-Graduação, Universidade Federal do ParáUniversidade Federal do Rio de JaneiroUniversidade Federal do ParáCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversidade Federal do Rio Grande do SulUniversity of Alberta
KeywordsMeta-analysisPeriodontitisMedicineMEDLINEBioinformaticsDentistryBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

This systematic review with meta-analysis aimed to evaluate the effect of antioxidants as an adjuvant in periodontitis treatment. The following databases were consulted: PubMed, Scopus, Web of Science, Cochrane, Lilacs, OpenGrey, and Google Scholar. Based on the PICO strategy, the inclusion criteria comprised interventional studies including periodontitis patients (participants) treated with conventional therapy and antioxidants (intervention) compared to patients treated only with conventional therapy (control) where the periodontal response (outcome) was evaluated. The risk of bias was evaluated using the Cochrane RoB tool (for randomized studies) and ROBINS-I tool (for nonrandomized studies). Quantitative data were analyzed in five random effects meta-analyses considering the following periodontal parameters: clinical attachment loss (CAL), plaque index (PI), gingival index (GI), bleeding on probing (BOP), and probing depth (PD). After all, the level of certainty was measured with the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) tool. Among the 1884 studies identified, only 15 interventional studies were according to the eligibility criteria and they were included in our review. From them, 4 articles presented a high risk of bias. The meta-analysis showed a statistically significant difference for CAL (SMD 0.29 (0.04, 0.55), <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>0.03</mml:mn></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msup><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>13</mml:mn><mml:mi>%</mml:mi></mml:math>), PI (SMD 0.41 (0.18, 0.64), <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>0.0005</mml:mn></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msup><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>47</mml:mn><mml:mi>%</mml:mi></mml:math>), and BOP (SMD 0.55 (0.27, 0.83), <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>0.0001</mml:mn></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:msup><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mi>%</mml:mi></mml:math>). The GRADE tool showed a moderate to high certainty in the quality of evidence depending on the clinical parameter and antioxidants used. These results suggest that the use of antioxidants is an adjunct approach to nonsurgical periodontal therapy which may be helpful in controlling the periodontal status.

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)0.020
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.032
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0250.042
Bibliometrics0.0120.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.158
GPT teacher head0.407
Teacher spread0.249 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations61
Published2019
Admission routes2
Has abstractyes

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