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Record W4403929652 · doi:10.5125/jkaoms.2024.50.5.243

Exploring the role of angiogenesis in fibrosis and malignant transformation in oral submucous fibrosis: a systematic review and meta-analysis

2024· review· en· W4403929652 on OpenAlexaboutno aff
R. Keerthika, Akhilesh Chandra, Dinesh Raja, Mahesh Khairnar, Rahul Agrawal

Bibliographic record

VenueJournal of the Korean Association of Oral and Maxillofacial Surgeons · 2024
Typereview
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOral submucous fibrosisAngiogenesisFibrosisPathologyMeta-analysisInternal medicine

Abstract

fetched live from OpenAlex

Angiogenesis is a crucial molecular driver of fibrosis in various inflammatory lesions. Oral submucous fibrosis (OSMF) is a chronic inflammatory fibrotic disorder with malignant potential. The angiogenetic pathways in OSMF remain obscure due to limited research, necessitating an in-depth review. This review aimed to illuminate the cryptic pathogenetic mechanisms of angiogenesis in the disease progression/fibrosis of OSMF and its malignant transformation, providing insights for improved treatment. Extensive literature searches were conducted across an array of databases until October 2023. Original research articles on angiogenesis in OSMF were included, and the risk of bias was assessed using the modified Newcastle-Ottawa scale. RevMan ver. 5.4 (Cochrane Collaboration) was used for data analysis. Thirty-four articles were included for qualitative synthesis and seven for quantitative analysis. Findings revealed that angiogenesis was significantly increased in early-stage OSMF but decreased as the disease advanced. It was also associated with the severity of epithelial dysplasia and malignant transformation. A random-effects model confirmed the upregulation of angiogenesis as a significant risk factor in early-stage fibrosis and malignant transformation. The mounting evidence reinforces that angiogenesis plays a crucial role in the progression of early-stage fibrosis of OSMF and its malignant transformation, opening avenues for diagnostic and therapeutic interventions.

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)0.003
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.087
GPT teacher head0.327
Teacher spread0.240 · 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 designMeta-analysis
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
Published2024
Admission routes1
Has abstractyes

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