Transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a rapidly evolving strategy for therapy of aortic stenosis. We describe the effect of the learning curve from the first 270 high-risk patients in Vancouver, Canada. METHODS: Patients underwent TAVI by transfemoral (63%) or transapical (37%) routes using balloon expandable valves. The experience was divided into the first half (FH, patients 1-135) and second half (SH, patients 136-270). RESULTS: The mean age was 83.2 ± 8 years (FH 83 ± 12 vs. SH 81 ± 7 years, P = 0.12). The mean Society of Thoracic Surgeons Score (STS) was 9.5% ± 5.2%- FH 10.5 vs. SH 8.5% (P = 0.01). The overall procedural success rate in the FH was 92.6%, improving to 97.8% in the SH (P = 0.05). The transfemoral procedural success improved-FH 89.3% to SH 98.8% (P = 0.01). The transapical procedural success remained high-FH 98.0% to SH 96.1% (P = 0.53). The overall 30-day mortality was 9.6%, improving from FH 13.3% to SH 5.9% (P = 0.04). In the transfemoral cases, 30-day mortality decreased by 56% [10.7-4.7%, P = 0.14], and similarly in transapical cases [17.6-7.8%, P = 0.14]. In-hospital stroke occurred in 3.3% (FH 3.7% vs. SH 2.9%, P = 0.74). The overall need for a new permanent pacemaker was 5.9% (FH 5.9% vs. SH 5.9%, P = 1). The overall major vascular injury rate was 6.7% (FH 8.1% vs. SH 5.2%, P = 0.33). The overall incidence of coronary vessel occlusion was 1.1% (FH 1.5 % vs. SH 0.7%, P = 0.56). Device embolization or failure to cross the valve was rare and largely seen in the FH only. Procedural experience (>135 procedures) was an independent predictor of 30-day survival (HR: 6.7, 95% CI: 1.2-18.1, P = 0.03). CONCLUSION: TAVI outcomes improve with experience and device development. While overall complication rates are low, scope remains to further reduce procedural adverse events.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".