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Record W4388804927 · doi:10.30699/mmlj17.6.1.42

Application of Dental Pulp Stem Cells in Modern Dentistry: A Narrative Review

2023· review· en· W4388804927 on OpenAlexvenueno aff
Melika Zanganeh Motlagh, Nikoo Hossein‐Khannazer, Nazanin Mahdavi, Pouyan Aminishakib, Massoud Vosough

Bibliographic record

VenueModern Medical Laboratory Journal · 2023
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsnot available
Fundersnot available
KeywordsDental pulp stem cellsStem cellNarrative reviewRegeneration (biology)DentistryCraniofacialMedicineCell biologyBiology

Abstract

fetched live from OpenAlex

Dental pulp stem cells (DPSCs) are a class of stem cells which originate from dental pulp tissue and possess multiple stem cell characteristics including high clonogenicity, differentiation capacity and immunomodulatory effects.DPSCs can be used in different stem cell-based therapy to treat a variety of diseases, such as autoimmune, orthopedic, and neurological diseases.Recent studies showed that DPSCs combined with biomaterials provides an effective approach to craniofacial bone regeneration and reconstruction of bone defects.Scaffolds improve cell attachment, proliferation, differentiation, and migration.In the present study we discuss different combination of biomaterials with DPSCs.

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
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.074
GPT teacher head0.412
Teacher spread0.338 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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