Impact of procedural capacity on transcatheter aortic valve replacement wait times and outcomes: a study of regional variation in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There has been rapid growth in the demand for transcatheter aortic valve replacement (TAVR), which has the potential to overwhelm current capacity. This imbalance between demand and capacity may lead to prolonged wait times, and subsequent adverse outcomes while patients are on the waitlist. We sought to understand the relationship between regional differences in capacity, TAVR wait times and morbidity/mortality on the waitlist. METHODS AND RESULTS: We modelled the effect of TAVR capacity, defined as the number of TAVR procedures per million residents/region, on the hazard of having a TAVR in Ontario from April 2012 to March 2017. Our primary outcome was the time from referral to a TAVR procedure or other off-list reasons on the waitlist/end of the observation period as measured in days. Clinical outcomes of interest were all-cause mortality, all-cause hospitalisations or heart failure-related hospitalisations while on the waitlist for TAVR. There was an almost fourfold difference in TAVR capacity across the 14 regions in Ontario, ranging from 31.5 to 119.5 TAVR procedures per million residents. The relationship between TAVR capacity and wait times was complex and non-linear. In general, increased capacity was associated with shorter wait times (p<0.001), reduced mortality (HR 0.94; p=0.08) and all-cause hospitalisations (p=0.009). CONCLUSIONS: The results of the present study have important policy implications, suggesting that there is a need to improve TAVR capacity, as well as develop wait-time strategies to triage patients, in order to decrease wait times and mitigate the hazard of adverse patient outcomes while on the waitlist.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it