MétaCan
Menu
Back to cohort
Record W6962567292 · doi:10.17605/osf.io/2uybn

Relationship between Serum Matrix Metalloproteinase-8 and Periodontitis: A PRISMA-Compliant Systematic Review and Meta-Analysis

2024· other· en· W6962567292 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPeriodontitisCochrane LibraryGrading (engineering)Systematic reviewMEDLINEMeta-analysis

Abstract

fetched live from OpenAlex

To determine whether serum matrix metalloproteinase-8 (MMP-8) level is elevated in periodontitis patients comparing to periodontally healthy subjects, we conducted a meta-analysis to assess its diagnostic value. This study aims to provide some enlightenment for solving the problem of higher prevalence and severity of systematic diseases in periodontitis patients, as well as help recognizing the potential of serum MMP-8 in the rapid diagnosis of periodontitis. A comprehensive search of Pubmed, Embase and Cochrane Library was conducted to retrieve comprehensive relevant articles. The methodological qualities of the included articles were evaluated following Newcastle-Ottawa Scale (NOS) grading system. The level of serum MMP-8 was converted to standard forms (Mean ± SD) and me-ta-analysis was conducted by Review Manager 5.4 software. Sensitivity analyses were performed by Stata 17.0 software. Our meta-analysis aims to help to show that serum MMP-8 level in periodontitis patients is higher than that in healthy subjects or not.

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)0.025
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.052
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.035
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.110
GPT teacher head0.400
Teacher spread0.290 · 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 designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkFrench-language works237,207