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Record W3108324633 · doi:10.1563/aaid-joi-d-20-00302

Role of Probiotics for the Treatment of Peri-Implant Mucositis in Patients With and Without Type 2 Diabetes Mellitus

2020· article· en· W3108324633 on OpenAlexaff

Bibliographic record

VenueJournal of Oral Implantology · 2020
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsCARE Canada
Fundersnot available
KeywordsType 2 Diabetes MellitusMucositisType 2 diabetesDiabetes mellitusDemographicsHemoglobin

Abstract

fetched live from OpenAlex

This study hypothesized that probiotic therapy (PT) does not offer additional benefits to mechanical debridement (MD) for the treatment of diabetic subjects with peri-implant mucositis (PM). This study compared the influence of PT as an adjunct to MD for the treatment of PM in type 2 diabetic and nondiabetic patients over a 12-month follow-up period. Patients with and without type 2 diabetes were included. PM patients were categorized into 2 groups based on the treatment procedure: (1) nonsurgical + PT and (2) nonsurgical MD alone. Demographics and education statuses were recorded. Gingival index (GI) and plaque index (PI), crestal bone loss (CBL), and probing depth (PD) were measured at baseline and after 6 and 12 months. Significant differences were detected with P < .01. The hemoglobin A1c level was significantly higher in patients with diabetes at all time durations than in patients without type 2 diabetes (P < .001). Baseline GI, PI, PD, and CBL were comparable in all groups. In patients with type 2 diabetes, there was no difference in PI, GI, PD, and CBL at 6- and 12-month follow-up. In patients without type 2 diabetes, there was a significant reduction in PI (P < .01), GI (P < .01), and PD (P < .01) at 6-month and 1-year follow-up as compared with baseline. In patients without type 2 diabetes, MD with or without adjunct PT reduced soft-tissue inflammatory parameters in patients with PM.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.031
GPT teacher head0.317
Teacher spread0.287 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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