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Record W4404163692 · doi:10.1016/j.job.2024.100587

CCN2: A potential contributor to gingival overgrowth

2024· review· en· W4404163692 on OpenAlexafffund
A. A. Fadl, Andrew Leask

Bibliographic record

VenueJournal of Oral Biosciences · 2024
Typereview
Languageen
FieldHealth Professions
TopicOral and gingival health research
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsDentistryMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Fibrotic responses in the gingiva are characterized by their hyperproliferative nature instead of scar tissue formation. Clinically, these conditions appear as "gingival overgrowth" (GO), which can be of drug-induced or genetic origin. Despite surgical removal, GO can recur. Therefore, non-invasive methods of treating GO are required. In other fibrotic systems, the matricellular protein CCN2 represents a potential therapeutic target. However, CCN2 has been relatively understudied in the context of GO. HIGHLIGHT: Herein, we describe what is known regarding CCN2 expression in GO and gingival fibroblasts. Specifically, CCN2 is induced by agents that promote fibrogenesis in the oral cavity, such as transforming growth factor-β, and drugs that promote GO, such as cyclosporine, nifedipine, and phenytoin. CONCLUSION: Although little is known regarding the possible function of CCN2 in GO, given the correlation between CCN2 expression and GO recurrence, we hope that this review will inspire further research on this topic.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.002

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 head0.287
GPT teacher head0.627
Teacher spread0.340 · 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.

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

Citations4
Published2024
Admission routes2
Has abstractyes

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