Prevalence, referral patterns, testing, and surgery in aortic valve disease: leaving women and elderly patients behind?
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: The prevalence of aortic valve disease is not well defined, and it is not known to what degree gender and age affect testing and surgery for this condition. The study aim was to describe the prevalence of aortic valve disease in the United States population by extrapolating from administrative claims databases; and to investigate differences associated with gender and age in referral, diagnostic testing, and aortic valve replacement (AVR). METHODS: A claims database of approximately five million privately insured beneficiaries and a 5% sample of Medicare beneficiaries were queried for patients with aortic valve disease. Prevalence was calculated by age group and gender, and extrapolated to the 2005 US population. The proportion of patients with a cardiologist or cardiovascular surgeon visit, performance of echocardiography or stress testing, and AVR within a year of diagnosis was determined. RESULTS: The extrapolated prevalence of aortic valve disease in the US in 2005 was 1.8% (approximately 5.2 million people); in persons aged > or =65 years, prevalence was 10.7%. Women were seen by a specialist, underwent diagnostic tests and underwent AVR at rates significantly lower than men, as did patients aged > or =80 years compared to those aged 65-79 years. AVR was performed at approximately half the rate in women (1.4%) compared to men (2.7%, p <0.001), and in patients aged > or =80 years (1.1%) compared to those aged 65-79 years (2.5%, p <0.001). CONCLUSION: In 2005, approximately 5.2 million adults in the US were estimated to have a diagnosis of aortic valve disease. Advanced age and female gender were associated with lower rates of specialist visits, diagnostic testing, and AVR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".