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Record W1978399607

Prophylaxis for infective endocarditis. Who needs it? How effective is it?

2000· article· en· W1978399607 on OpenAlexaff
Natasha Press, Valentina Montessori

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInfective endocarditisMedicineEndocarditisAntibiotic prophylaxisIntensive care medicineAntibioticsAmoxicillinRandomized controlled trialDiseaseViridans streptococciClinical trialHeart diseaseInternal medicineMicrobiologyStreptococcusBacteria
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To review guidelines for using antibiotic prophylaxis to prevent infective endocarditis, and to present recent changes and controversies regarding these guidelines. QUALITY OF EVIDENCE: Data are from physiologic and in vitro studies, as well as studies of animal models, and from retrospective analyses of human endocarditis cases. Systematic reviews and guidelines are also examined. As no randomized clinical trials have examined prophylaxis for bacterial endocarditis, many recommendations presented are based on consensus guidelines. MAIN MESSAGE: Antibiotic prophylaxis to prevent bacterial endocarditis should be used in high- and moderate-risk patients with cardiac disease. It should be given before procedures in which bacteremias are likely with organisms that cause endocarditis, such as viridans streptococci. For most procedures, a single dose of amoxicillin (2 g by mouth 1 hour before the procedure) is sufficient to ensure adequate serum levels before and after the procedure. CONCLUSION: Infective endocarditis continues to have high rates of morbidity and mortality. Antibiotic prophylaxis, therefore, is important to combat this preventable disease. For high- and moderate-risk patients with cardiac disease, the cost-benefit ratio favours prophylaxis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 designOther design
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

Citations3
Published2000
Admission routes1
Has abstractyes

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