Transcatheter aortic valve implantation. First experience of minimally invasive treatment in the Republic of Moldova
Bibliographic record
Abstract
Background: Aortic stenosis (AS) is the most prevalent primary heart valve disease and the third most prevalent cardiovascular disease in the world, having a severe and slowly progressive evolution. The implementation of Transcatheter Aortic Valve Implantation (TAVI) is necessary because the population is constantly ageing, and most elderly people have various comorbidities, which places them among patients with high surgical risk. The aim was to report immediate and 9 months follow-up results of first TAVI implantations in the Republic of Moldova. Material and methods: Three patients with severe symptomatic AS and high surgical risk underwent TAVI implantation using Portico valves. Results: The mean age was 76.7 ± 1.2 years, 2 women and 1 man. The mean Logistic EuroSCORE II was 5.68 ± 0.67%. Procedural success was achieved in all (100%) patients. After the intervention, it was shown that the GP mean decreased by an average of 20.27±1.61 mmHg, the Vmax of the jet through AoV decreased on average by 2.05±0.19 m/s, and they were maintained all over the nine months follow-up period. Quality of life, using the TASQ (Toronto Aortic Stenosis Quality of Life) questionnaire, was improved by 62.3 ± 2.2 points. Conclusions: TAVI, as a new minimally invasive method of treatment for aortic valve stenosis appears to be safe, with a low rate of early and late complications in elderly patients at high surgical risk, with good clinical outcomes at 9 months. Its implementation in the Republic of Moldova is welcome in the conditions of continuously ageing population and the prevalence of associated comorbidities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".