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Record W4417455197 · doi:10.69598/sehs.19.25050012

Effects of fluoroquinolone on mitral and aortic regurgitation: A systematic review and meta-analysis

2025· article· W4417455197 on OpenAlexaboutno aff
Yawee Sricoth, Wiwat Thavornwattanayong

Bibliographic record

VenueScience, Engineering and Health Studies · 2025
Typearticle
Language
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsnot available
Fundersnot available
KeywordsMitral regurgitationMitral valveAortic valveRegurgitation (circulation)AmoxicillinEndocarditis

Abstract

fetched live from OpenAlex

This study aimed to systematically review and meta-analyze the effects of fluoroquinolones on mitral and aortic regurgitation. Seven electronic databases were searched for relevant research data published in English and Thai between the database’s inception and September 30, 2022, including additional records from Silpakorn University and Chulalongkorn University’s online theses databases. This systematic review included studies that compared the effects of fluoroquinolones and other antibiotic classes on the occurrence of mitral and aortic valve regurgitation. Subsequently, the quality of the research was evaluated using the Newcastle-Ottawa Quality Assessment Scale. Of the 2,891 articles identified, two eligible studies were included in the analysis. The meta-analysis examined different exposure periods and discovered that individuals who received fluoroquinolones within 30 days and within one year had a similar risk of developing mitral and aortic valve regurgitation compared to those who received other groups of antibiotics. Specifically, the risk of mitral and aortic regurgitation associated with fluoroquinolones was comparable to that of amoxicillin and macrolides. Overall, the meta-analysis found no association between fluoroquinolone use and the development of mitral and aortic regurgitation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.050
GPT teacher head0.397
Teacher spread0.347 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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