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Record W2419320782 · doi:10.1097/hco.0000000000000260

Year in review

2016· review· en· W2419320782 on OpenAlexaff
Meng‐Ta Tsai, Gilbert H.L. Tang, Gideon Cohen

Bibliographic record

VenueCurrent Opinion in Cardiology · 2016
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineValve replacementRegurgitation (circulation)PercutaneousStroke (engine)CardiologyIntensive care medicineInternal medicineSurgeryStenosis

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Transcatheter aortic valve replacement (TAVR) has been effective in prohibitive/high-risk patients. Expansion toward intermediate or even low-risk patients has been proposed. A review of recent developments will help understand current trends and issues. RECENT FINDINGS: The 5-year results from the PARTNER I trial and 2-year results of the CoreValve US Pivotal Trial, together with national registries, confirmed the long-term efficacy and durability of TAVR. Studies including PARTNER II, ADVANCE, and The German Aortic Valve Registry showed short-to mid-term success in intermediate-risk patients. Comparison of balloon-expandable and self-expanding valves in the CHOICE trial associated specific outcome differences with specific valve types, suggesting a more customized approach in valve selection. Short-term results of newer-generation valves have demonstrated excellent safety and efficacy with improved designs. Studies on TAVR-specific complications, such as conduction block and arrhythmia, paravalvular aortic regurgitation, and stroke, have renewed ideas about their prognosis, treatment, and prevention. Conscious sedation percutaneous TAVR has become more popular, with excellent outcomes and improved cost savings. SUMMARY: TAVR has been accepted as an effective treatment even for intermediate-risk patients. This article aims to review the most recent results and essential issues.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.133
GPT teacher head0.514
Teacher spread0.381 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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