CHANGES IN OUTCOMES OVER TIME IN INTERMEDIATE-RISK PATIENTS TREATED FOR SEVERE AORTIC STENOSIS
Bibliographic record
Abstract
Background:The advent of TAVR changed the practice for treating patients with severe aortic stenosis. Heart-Teams improved their decision-making process to refer patients to the best and safest treatment. Evidence allowed centers to increase funding and TAVR volume and extend indications to different risk category of patients. This study evaluates the outcomes of intermediate-risk patients treated for severe aortic stenosis in an academic center. Methods:Between 2012 and 2019, 812 patients with aortic stenosis underwent TAVR or SAVR. A propensity score-matching analytic strategy was used to balance groups and adjust for time periods. Outcomes were recorded according to the Society of Thoracic Surgeons Guidelines; primary outcome being 30-day mortality and secondary outcomes being perioperative course and complications. Results:No difference in mortality was seen but complications differed: more postoperative transient ischemic attacks, permanent pacemaker implantations and perivalvular leaks in the transcatheter group, while more acute kidney injuries, atrial fibrillation, delirium, postoperative infections and bleeding, tamponade and need for reoperation in the surgical group as well as longer hospital length-of-stay. However, over the years, morbidities/mortality decreased for all patients treated for aortic stenosis. Conclusions:Data showed an improvement in morbidities/mortality for intermediate risk patients treated with SAVR or TAVR. Increased funding allowed for higher TAVR volume by increasing access to this technology. Also, the difference in complications could impact healthcare cost. By incorporating important metrics such as length-of-stay, readmission rates and complications into decision-making, the Heart-Team can improve clinical outcomes, healthcare economics and resource utilization.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".