Abstract 514: Development and Validation of Risk Model for Predicting Prevalence of Peripheral Artery Disease in a Low to Intermediate Risk Population
Bibliographic record
Abstract
Background: Peripheral artery disease (PAD) is associated with impaired quality of life and significant cardiovascular morbidity and mortality, yet remains under recognized and under diagnosed. Objectives: This study sought to develop and validate a risk model for the identification of individuals at risk for PAD that could be useful in the clinical setting. Methods: Twenty three variables assessed in ≈3.2 million self-referred participants without established cardiovascular disease from 2003 to 2008 who completed a medical and lifestyle questionnaire in the United States were evaluated by screening ankle brachial indices <0.90 for PAD. Subjects were divided into a derivation cohort (1.57 million) and a validation cohort (1.57 million). Lasso variable selection was used in the derivation cohort to develop the best-fitting parsimonious prediction models. Discrimination and calibrations was evaluated using the C statistic and the Hosmer-Lemeshow calibration statistic. Results: The overall prevalence of PAD was 3.96%. Using lasso variable selection, 11 variables were included in complex best-fitting model: age, sex, race, marital status, BMI group, smoking status, hypertension, diabetes, family history of PAD, physical activity, and inter-arm systolic blood pressure difference. In the validation cohort, the C-statistics for this model was .746 and the calibration was excellent (P=0.38; no significant deviation between predicted and observed outcomes). The current risk score has improved discrimination and calibration compared with other existing risk scores for PAD. Conclusion: This robust PAD risk calculator derived from a diverse population across the US provides a good risk estimate of PAD and is anticipated to assist in identifying subjects at risk for PAD.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".