Abstract 179: Temporal Trends and Clinical Consequences of Wait-Times for Trans-Catheter Aortic Valve Replacement: A Population Based Study
Bibliographic record
Abstract
Background: Trans-catheter aortic valve replacement (TAVR) represents a paradigm shift in the therapeutic options for patients with severe aortic stenosis. However, rapid and exponential growth in TAVR demand may overwhelm capacity, translating to inadequate access and prolonged wait-times. Our objective was to evaluate temporal trends in TAVR wait-times and the associated clinical consequences. Methods: In this population-based study in Ontario, Canada, we identified all TAVR referrals from April 1, 2010 to March 31, 2016. The primary outcome was the median total wait-time from referral to procedure. Piecewise regression analyses were performed to assess temporal trends in TAVI wait-times, before and after provincial reimbursement in September 2012. Clinical outcomes included all-cause death and heart failure hospitalizations while on the wait-list. Results: The study cohort included 4,461 referrals, of which 50% led to a TAVR, 39% were off-listed for other reasons and 11% remained on the wait-list at the conclusion of the study. For patients who underwent a TAVR, the estimated median wait-time in the post-reimbursement period stabilized at 82-84 days, and has remained unchanged since September 2012. The cumulative probability of wait-list mortality and heart failure hospitalization was 4.3% and 14.7% respectively, with a relatively constant increase in events with increased wait-times. Conclusion: Post-reimbursement wait-time has remained unchanged for patients undergoing a TAVR procedure, suggesting the increase in capacity has kept pace with the increase in demand. The current wait-time of almost 3 months is associated with important morbidity and mortality, suggesting a need for greater capacity and access.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".