Estimating angina prevalence in a managed care population.
Bibliographic record
Abstract
Estimations of angina prevalence were calculated using managed care administrative data and applying 3 angina-related definitions. The definitions comprised angina pectoris diagnosis codes, diagnosis and procedure codes signifying the broader condition of coronary artery disease (CAD), including angina pectoris, and diagnosis codes for the symptom of chest pain. Prevalence rates were calculated in 2000, 2001, and the combined period of 2000 and 2001 for each definition based on the number of members with at least 1 day of eligibility in each period. Results were compared with published estimates and projected to the US population. The prevalence rates per 1000 people for angina pectoris in 2000, 2001, and 2000--2001 were 12.3, 14.0, and 17.5, respectively. The prevalence rate is higher in the combined 2-year period primarily because there is little duplication in patients with angina who appear in both years, but there is significant overlap in the overall (denominator) population eligible in both years. For CAD the rates were 52.2, 59.9, and 65.4, respectively, and for chest pain they were 63.4, 75.8, and 93.4, respectively. Rates were higher in men versus women and in each successive age group. These gender and age results were observed in the projections to the US population. By comparison, the American Heart Association (AHA) estimates angina pectoris prevalence to be 35 per 1000 in 2001. The lower managed care rate for angina pectoris may reflect differences in data capture (ie, self-reported data for AHA vs claims submitted for reimbursement for managed care). AHA estimates are higher for women versus men while the managed care estimates show the opposite trend. Prevalence of angina in the United States is substantial. With the aging of the US population, numbers of patients with angina presenting to the healthcare system can be expected to increase, further adding to the cost burdens facing managed care.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".