Abstract 15746: Trends in the Hospitalization Rates and Outcomes of Patients With Aortic Stenosis From 2004 to 2013
Bibliographic record
Abstract
Background: An aging population and the introduction of transcatheter aortic valve replacement (TAVR) have led to a major shift in how patients with aortic stenosis (AS) are managed. Although there is a need to understand the epidemiological aspects of AS for future resource planning, few studies have characterized recent trends in hospitalization and outcomes of these patients. Methods: We performed an observational study using population-based data in Ontario, Canada. Patients with a first hospitalization for a primary or secondary diagnosis of AS between 2004 and 2013 were included. The annual rates of AS-related hospitalization were determined and standardized by the Ontario population. Rates of aortic valve intervention and all-cause mortality at 30 days and 1 year after index hospitalization were assessed. The Cochrane-Armitage test was used to evaluate temporal trends. Logistic regression models were used to evaluate adjusted mortality. Results: A total of 37,970 patients were hospitalized with AS during the study period. The age- and sex-standardized hospitalization rate for AS increased from 36 to 39 per 100,000 between 2004 and 2013 (p Conclusion: Over the past decade when TAVR was introduced, rates of AS hospitalization have significantly increased in the elderly, beyond that expected with population growth. While rates of intervention have also increased, this has not been associated with a change in mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".