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Record W1972115811 · doi:10.1016/s1474-5151(10)60022-9

35 Oral Transcatheter Aortic Valve Implantation: Supporting Care from Referral to Follow-Up

2010· article· en· W1972115811 on OpenAlexaff
Sandra Lauck, C. Galte, John G. Webb

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineReferralAortic valveCardiologyIntensive care medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

Background: Transcatheter aortic valve implantation (TAVI) is fast becoming a treatment option for people with severe aortic stenosis. The rapid development of transcatheter approaches offers promising treatment options for non-surgical candidates, and may in the future present a safe and less invasive alternative to surgical valve replacement. As TAVI gains acceptance and popularity, it is essential that nurses and allied health professionals keep pace with identifying and addressing the needs of patients and programmes to prevent gaps in care. Outline: The presentation will describe the care map developed by the interdisciplinary team to support the trajectory of care from referral to follow-up for the more than 200 patients who have undergone TAVI procedures at our centre. The various components of the TAVI care map will be outlined. The importance of the assessment of patient-reported outcomes, including physical, mental and social function, and neurocognitive status in the elderly cardiac patient will be stressed. The findings of the evaluation of the care map in the initial 24 hours following the procedure will be presented. Recommendations for programme development, including screening process, pre-procedure teaching and early discharge planning will be discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.008
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 head0.019
GPT teacher head0.320
Teacher spread0.301 · 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 designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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