The clinical and cost burden of coronary calcification in a Medicare cohort: An economic model to address under-reporting and misclassification
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: Coronary artery calcification (CAC) is a well-established risk factor for the occurrence of adverse ischemic events. However, the economic impact of the presence of CAC is unknown. OBJECTIVES: Through an economic model analysis, we sought to estimate the incremental impact of CAC on medical care costs and patient mortality for de novo percutaneous coronary intervention (PCI) patients in the 2012 cohort of the Medicare elderly (≥65) population. METHODS: This aggregate burden-of-illness study is incidence-based, focusing on cost and survival outcomes for an annual Medicare cohort based on the recently introduced ICD9 code for CAC. The cost analysis uses a one-year horizon, and the survival analysis considers lost life years and their economic value. RESULTS: For calendar year 2012, an estimated 200,945 index (de novo) PCI procedures were performed in this cohort. An estimated 16,000 Medicare beneficiaries (7.9%) were projected to have had severe CAC, generating an additional cost in the first year following their PCI of $3500, on average, or $56 million in total. In terms of mortality, the model projects that an additional 397 deaths would be attributable to severe CAC in 2012, resulting in 3770 lost life years, representing an estimated loss of about $377 million, when valuing lost life years at $100,000 each. CONCLUSIONS: These model-based CAC estimates, considering both moderate and severe CAC patients, suggest an annual burden of illness approaching $1.3 billion in this PCI cohort. The potential clinical and cost consequences of CAC warrant additional clinical and economic attention not only on PCI strategies for particular patients but also on reporting and coding to achieve better evidence-based decision-making.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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