Impact of pre-procedural requirements on time to aortic valve replacement: Transcatheter AVR vs surgical AVR
Bibliographic record
Abstract
Background/objective: This study aims to understand the extent that cardiac specialist visits and imaging requirements contribute to the difference in time to Aortic valve replacement (AVR) stratified by approach transcatheter AVR (TAVR) and surgical AVR (SAVR). Methods: Optum Market Clarity Data was used to identify patients with clinically significant AS (CSAS) who received an AVR between 2016 and 2023 and whose AVR occurred within two years of their CSAS diagnosis. Patient characteristics were measured at baseline; pre-procedural factors, including the number of cardiac specialist visits and imaging events, were measured from CSAS diagnosis to AVR (TAVR vs SAVR). Stepwise generalized linear models were used to assess whether the number of cardiac specialist visits and imaging events contribute to the differences in time to TAVR and SAVR, after adjusting for baseline characteristics. Results: Of the 14,225 patients in the cohort, 42 % received a TAVR. Compared to the SAVR cohort, the TAVR cohort was, on average, more male, older, sicker, and had more Medicare enrollees. TAVR patients had approximately two times more cardiac specialist visits (3.73 vs 6.37) and imaging events (1.18 vs 2.07) than SAVR patients. Time to TAVR is 65 days longer (RR = 1.77, 1.67-1.87) than SAVR, after adjustment for patient characteristics. This difference reduces to 11 days (RR = 1.12, 1.07-1.17) after accounting cardiac specialist encounters and imaging events. Discussion: Pre-procedural encounters significantly contribute to the longer time to AVR for TAVR patients. Findings suggest a need for streamlining the pre-procedural process for TAVR to enhance timely care delivery for CSAS patients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".