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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.074
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