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Record W4387056282 · doi:10.1016/j.athplu.2023.09.004

Fetuin-A levels in association with calcific aortic valve disease: A meta-analysis

2023· article· en· W4387056282 on OpenAlexaboutno aff
Muhammad Omar Larik

Bibliographic record

VenueAtherosclerosis Plus · 2023
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisInternal medicineAortic valveBiomarkerFetuinCardiology

Abstract

fetched live from OpenAlex

Background and aims: Calcific aortic valve disease (CAVD) is a common valvular disease, prevalent particularly within the older age groups. The potential use of biomarkers in diagnosing and assessing the severity of CAVD, in supplementation with imaging techniques, has recently gained momentum within the field of cardiovascular medicine. Therefore, a meta-analysis was performed that assessed the association between the fetuin-A levels, and the presence of CAVD. Methods: PubMed and Cochrane were searched from inception to April 2023. Risk of bias was assessed using the Newcastle-Ottawa scale for cohort studies. Results: This analysis includes a total of 3,280 patients with CAVD, and 7,505 patients as control, resulting in the pooling of 10,785 patients in this meta-analysis. It was observed that the circulating levels of fetuin-A were significantly lowered in patients with CAVD (SMD: -0.20; 95% CI: -0.39, -0.02; P = 0.03). Moreover, the analysis revealed that fetuin-A levels had no significant association with CAVD in patients suffering from kidney disease (SMD: 0.20; 95% CI: -0.46, 0.85; P = 0.56). Conclusion: While initial results demonstrate the potential effectiveness, further research is essential in order to arrive at a robust conclusion regarding the use of fetuin-A as a diagnostic biomarker for calcific aortic valve disease.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
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 head0.110
GPT teacher head0.328
Teacher spread0.218 · 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 designObservational
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

Citations7
Published2023
Admission routes1
Has abstractyes

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