Bibliographic record
Abstract
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".