The road to a transcatheter edge-to-edge repair: patient experiences leading up to the procedure and in the early recovery period
Bibliographic record
Abstract
AIMS: Mitral valve transcatheter edge-to-edge repair (TEER) is a minimally invasive treatment option for patients with severe symptomatic mitral regurgitation who are at increased risk for cardiac surgery and are receiving optimal medical therapy. Little is known about patients' perspectives on their journey of care, including their experiences leading up to treatment and their early recovery period. The aim of this study was to explore patients' experiences of their journey to TEER and their perspectives on early recovery. METHODS AND RESULTS: We conducted a qualitative study using interpretive description. A purposive sample of 12 patients from a purposive sample, 3-6 monthspost-TEER procedure, were recruited from a tertiary hospital. The median age of the patients was 79 years, with seven males and five females. Data collection included semi-structured interviews over the phone. Data analysis followed an iterative process and utilized thematic analysis. There were four central themes highlighting the experiences of the patients leading up to their procedure: (i) escalating challenges with everyday life; (ii) plummeting losses; (iii) choosing and readiness to proceed with TEER; and (iv) the long and uncertain waiting time. The theme-improved health status highlights the experiences of patients in their early recovery. CONCLUSION: Patients' experiences of waiting for TEER are complex and involve multifaceted challenges related to their worsening cardiac symptoms and navigating the healthcare system. Therefore, care pathways must be put in place to provide continuity of care and support.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".