35 Oral Transcatheter Aortic Valve Implantation: Supporting Care from Referral to Follow-Up
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".