MétaCan
Menu
Back to cohort
Record W1970166917 · doi:10.1097/jcn.0b013e318217d288

Transcatheter Aortic Valve Implantation Outcomes

2012· review· en· W1970166917 on OpenAlexaff
Marion E. McRae, Marnie Rodger

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2012
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineStenosisAortic valve stenosisAortic valve replacementIntensive care medicineQuality of life (healthcare)Randomized controlled trialClinical trialAortic valveGold standard (test)Psychological interventionValve replacementCardiologyInternal medicineNursing

Abstract

fetched live from OpenAlex

Aortic stenosis is a common valvular pathological finding in older adults. Currently, aortic valve replacement is the gold-standard treatment for severe symptomatic aortic stenosis. However, patients with advanced age and multiple comorbidities carry a significant operative risk. Transcatheter aortic valve implantation (TAVI) was developed with the goal of offering a less invasive alternative to symptomatic high-risk patients with aortic stenosis. Since the first successful TAVI procedure in 2002, TAVI has been used as a treatment option for patients at very high or prohibitive surgical risk in clinical feasibility trials, registries, and in ongoing randomized controlled trials. There are 2 transcatheter valves in widespread clinical application, with several others in different stages of development. This article provides an overview of TAVI outcomes including insertion options, procedural outcomes, morbidity, valve durability, short- to medium-term survival, and quality of life to guide nursing care interventions. Enhancing nurses' knowledge of the risks, benefits, and potential complications of TAVI will empower nurses in their role as patient advocates and educators and improve patient outcomes. Gaps in the current TAVI research literature are identified.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.041
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 head0.048
GPT teacher head0.387
Teacher spread0.339 · 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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Cardiovascular NursingSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207