Facilitating transcatheter aortic valve implantation in the era of COVID-19: Recommendations for programmes
Bibliographic record
Abstract
The COVID-19 pandemic continues to significantly impact the treatment of people living with aortic stenosis, and access to transcatheter aortic valve implantation. Transcatheter aortic valve implantation (TAVI) programmes require unique coordinated processes that are currently experiencing multiple disruptions and are guided by rapidly evolving protocols. We present a series of recommendations for TAVI programmes to adapt to the new demands, based on recent evidence and the international expertise of nurse leaders and collaborators in this field. Although recommended in most guidelines, the uptake of the role of the TAVI programme nurse is uneven across international regions. COVID-19 is further highlighting why a nurse-led central point of coordination and communication is a vital asset for patients and programmes. We propose an alternative streamlined evaluation pathway to minimize patients' pre-procedure exposure to the hospital environment while ensuring appropriate treatment decision and shared decision-making. The competing demands created by COVID-19 require vigilant wait list management, with risk stratification, telephone surveillance and optimized triage and prioritization. A minimalist approach with close scrutiny of all parts of the procedure has become an imperative to avoid any complications and ensure patients' accelerated recovery. Lastly, we outline a nurse-led protocol of rapid mobilization and reconditioning as an effective strategy to facilitate safe next-day discharge home. As the pandemic abates, TAVI programmes must facilitate access to care without compromising patient safety, enable hospitals to manage the competing demands created by COVID-19 and establish new processes to support patients living with valvular heart disease.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".