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Medical dental prophylaxis of endocarditis

2004· review· en· W2169802752 on OpenAlexaff
Regina C. Basilio, Francisco Eugênio Loducca, Paulo C. Haddad

Bibliographic record

VenueThe Brazilian Journal of Infectious Diseases · 2004
Typereview
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMedicineBacteremiaEndocarditisIntensive care medicineAntibioticsAntibiotic prophylaxisInfective endocarditisImmunologySurgery

Abstract

fetched live from OpenAlex

Antibiotics have long been the main reason for the increase in man's longevity. Since their discovery, man has tried to reduce the level of infection by treating with antibiotics. At the same time, prophylactic use has been suggested, although this is controversial. Their routine use is not recommended, and empirical treatments at non-therapeutic doses, and indiscriminately, should be avoided, because they may become dangerous and harmful, causing among other things, the prevalence of resistant microorganisms and the eventual potentiation of an increase in morbid states. Infectious endocarditis is a systemic pathology that can start with a bacteremia, which comes either from dental procedures or/and chronic processes that already existed. Its etiopathogeny consists of a combination of bacteremia and two other factors: Cardiac injury, which can be congenital or/and acquired, and a debilitated immunological system (patients who have transplanted organs, or those who have auto-immune diseases, such as pemphigus vulgaris, systemic lupus erythematosus). The main goal is to prevent or to fight against the transient bacteremia, reducing its intensity and duration, and also to kill the bacteria in at-risk patients. In this way, infectious endocarditis can be prevented; the dental surgeon plays an important role in the prevention of this condition, which joins medical and dental aspects. This can be done by antibiotic prophylaxis. The dentist needs to be acquainted with the medical protocols of the heart health societies.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.315
Teacher spread0.302 · 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
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

Citations6
Published2004
Admission routes1
Has abstractyes

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