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Record W2797663642 · doi:10.1515/cclm-2017-1018

Salivary biomarkers and cardiovascular disease: a systematic review

2018· review· en· W2797663642 on OpenAlexaboutno aff
Vishal Gohel, Judith A. Jones, Carolyn J. Wehler

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2018
Typereview
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsnot available
Fundersnot available
KeywordsSalivaMedicineDiseaseInternal medicineCreatine kinasePathologyBioinformaticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this systematic review is to summarize the literature examining associations between salivary biomarkers and cardiovascular disease (CVD) status. CONTENTS: An advanced search was conducted using MeSH terms related to salivary biomarkers and CVD, and entered into the PubMed, Web of Science, and Google Scholar search databases. Four hundred and thirty-three records were narrowed to 22 accepted articles. Included titles were assessed for quality using the Newcastle-Ottawa scale, and ranked into categories of low, moderate, or high. SUMMARY: A total of 40 salivary biomarkers were analyzed among accepted articles. The most studied markers were salivary creatine kinase isoform MB, C-reactive protein (CRP), matrix metalloproteinase-9, troponin I, myeloperoxidase, myoglobin, and brain natriuretic peptide. Salivary CRP provided the most consistent trends. Statistically significant increases of salivary CRP were present with CVD in every study that analyzed it. The remaining six markers demonstrated varying patterns. OUTLOOK: Existing studies provide insufficient data to draw definitive conclusions. Current research shows that there is an association between some salivary biomarkers and CVD, but the details of existing studies are conflicting. Despite inconclusive results, the diagnostic potential of saliva shows promise as a non-invasive means of cardiovascular risk assessment.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.070
GPT teacher head0.380
Teacher spread0.309 · 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 designSystematic review
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

Citations48
Published2018
Admission routes1
Has abstractyes

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