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Record W2412607644 · doi:10.1097/hco.0000000000000269

Update on rheumatic heart disease

2016· review· en· W2412607644 on OpenAlexaff
Bobby Yanagawa, Jagdish Butany, Subodh Verma

Bibliographic record

VenueCurrent Opinion in Cardiology · 2016
Typereview
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsToronto General HospitalSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicinePathogenesisRheumatic feverIntensive care medicineHeart diseaseSubclinical infectionDiseaseImmunologyCarditisMolecular mimicryBioinformaticsImmune systemCardiologyPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose is to provide a broad overview of the current state of knowledge of pathogenesis, diagnosis, and management of rheumatic heart disease (RHD). RECENT FINDINGS: Studies on pathogenesis of RHD have focused on autoimmunity because of molecular mimicry between the streptococcal M antigen α-helical coiled-coil structure and sarcomeric proteins such as myosin and tropomyosin. More recently, nonsarcomeric autoantigens, endothelial injury and the innate immune system have been proposed to play key roles in the pathogenesis of RHD. In the 2015 revised Jones Criteria, the importance of echocardiography and subclinical carditis in the diagnosis of acute rheumatic fever is highlighted. Experimental studies with targeted anti-inflammatory therapeutics have been largely unsuccessful and the only established treatment is still lifelong antibiotics. Efforts to improve patient selection and outcomes with percutaneous mitral balloon valvuloplasty are ongoing. With regard to surgical management, several groups have demonstrated excellent operative and midterm outcomes from valve repair as opposed to valve replacement. SUMMARY: There are still many unanswered questions regarding RHD pathogenesis. The only accepted medical treatment is still long-term antibiotic therapy, whereas advances in mitral repair techniques have led to successful durable repairs being performed in high-volume, expert centers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0000.002

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 head0.149
GPT teacher head0.459
Teacher spread0.310 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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