Proper Selection Does Make the Difference: A Propensity-Matched Analysis of Percutaneous and Surgical Cut-Down Transfemoral TAVR
Bibliographic record
Abstract
Background. Transcatheter aortic valve replacement (TAVR) is an established technique to treat severe symptomatic aortic stenosis patients with a wide range of surgical risk. Currently, the common femoral artery is the first choice as the main access route for the procedure. The objective of this observational study is to report our experience on percutaneous and surgical cut-down transfemoral TAVRs comparing the two approaches. Methods. From January 2014 to January 2019, five hundred eleven consecutive patients underwent TAVR for severe symptomatic aortic stenosis. We analyzed only elective transfemoral procedures. After propensity score-matching based on age, sex, EuroSCORE II, mean aortic gradient, and left ventricular ejection fraction, we obtained two homogeneous populations: surgical cut-down (n = 119) and percutaneous (n = 225), which were labeled Group 1 and Group 2, respectively. Results. The main findings were that there were no significant procedural outcome differences between the two groups, but Group 2 patients had a shorter length of hospital stay and were more frequently discharged home. At follow-up, Group 1 patients had lower survival rates. Conclusions. An accurate preoperative assessment of the femoral access is mandatory to achieve satisfactory outcomes with transfemoral TAVRs. Nevertheless, the percutaneous approach allows shorter in-hospital stay and the need for rehabilitation, thus potentially decreasing the costs of the procedure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".