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Record W4379052900 · doi:10.3389/fdmed.2023.1163562

Innovative biomaterials for the treatment of periodontal disease

2023· review· en· W4379052900 on OpenAlexaff
Yi Zhu, Tao Chen, Cynthia Goh, Annie Shrestha

Bibliographic record

VenueFrontiers in Dental Medicine · 2023
Typereview
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsMount Sinai HospitalUniversity of Toronto
FundersAmerican Association of Endodontists Foundation
KeywordsRegeneration (biology)PeriodontitisDebridement (dental)MedicineDentistryBone resorptionRegenerative medicineIntensive care medicineStem cellBiologyInternal medicine

Abstract

fetched live from OpenAlex

Periodontitis is a multifactorial disease that involves the destruction of hard and soft tissues surrounding the tooth. Routine periodontal treatment includes mechanical debridement (surgical and non-surgical) and the systemic administration of antibiotics. In contrast, severe and chronic periodontitis involves aggressive tissue destruction and bone resorption, and the damage is usually irreversible. In these severe cases, bone grafts, the delivery of growth hormones, and guided tissue regeneration can all be used to stimulate periodontal regeneration. However, these approaches do not result in consistent and predictable treatment outcomes. As a result, advanced biomaterials have evolved as an adjunctive approach to improve clinical performance. These novel biomaterials are designed to either prolong the release of antibacterial agents or osteogenic molecules, or to act as immunomodulators to promote healing. The first half of this review briefly summarizes the key immune cells and their underlying cellular pathways implicated in periodontitis. Advanced biomaterials designed to promote periodontal regeneration will be highlighted in the second half. Finally, the limitations of the current experimental design and the challenges of translational science will be discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.106
GPT teacher head0.422
Teacher spread0.316 · 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 designNot applicable
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

Citations17
Published2023
Admission routes1
Has abstractyes

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