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Record W4210698172 · doi:10.1186/s13287-022-02700-x

A new direction in managing avulsed teeth: stem cell-based de novo PDL regeneration

2022· review· en· W4210698172 on OpenAlexaff
Hacer Aksel, Xiaofei Zhu, Philippe Gauthier, Wenjing Zhang, Adham A. Azim, George T.‐J. Huang

Bibliographic record

VenueStem Cell Research & Therapy · 2022
Typereview
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsUniversité Laval
FundersNational Institute of Dental and Craniofacial ResearchHealth Science Center, University of TennesseeNational Institutes of HealthAmerican Association of Endodontists Foundation
KeywordsPeriodontal fiberRegeneration (biology)DentistryTooth ReplantationMedicineStem cellRegenerative medicineReplantationOrthodonticsSurgeryBiologyPathology

Abstract

fetched live from OpenAlex

Management of avulsed teeth after replantation often leads to an unfavorable outcome. Damage to the thin and vulnerable periodontal ligament is the key reason for failure. Cell- or stem cell-based regenerative medicine has emerged in the past two decades as a promising clinical treatment modality to improve treatment outcomes. This concept has also been tested for the management of avulsed teeth in animal models. This review focuses on the discussion of limitation of current management protocols for avulsed teeth, cell-based therapy for periodontal ligament (PDL) regeneration in small and large animals, the challenges of de novo regeneration of PDL on denuded root in the edentulous region using a mini-swine model, and establishing a prospective new clinical protocol to manage avulsed teeth based on the current progress of cell-based PDL regeneration studies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.377
GPT teacher head0.527
Teacher spread0.150 · 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 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

Citations21
Published2022
Admission routes1
Has abstractyes

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