Predictive Performance and Optimal Cut-Off Points of Blood Pressure for Identifying Arteriosclerosis among Adults in Eastern China
Bibliographic record
Abstract
This study aimed to assess the predictive performance and establish optimal cut-off points of blood pressure for identifying arteriosclerosis in eastern Chinese adults. Brachial–ankle pulse wave velocity (baPWV) was utilized to evaluate arteriosclerosis. The predictive performance of blood pressure for arteriosclerosis was determined by the area under the curve (AUC) of receiver operating characteristics; the optimal blood pressure cut-off points were determined by Youden’s index. A logistic regression model was used to acquire the odds ratio (OR) of blood pressure for arteriosclerosis. The AUCs of blood pressure for identifying arteriosclerosis were 0.868 (95%CI: 0.860–0.875) for systolic blood pressure (SBP) and 0.835 (95%CI: 0.827–0.843) for diastolic blood pressure (DBP), both p < 0.01. The AUCs of women were higher than that of men (0.903 vs. 0.819 for SBP; 0.847 vs. 0.806 for DBP; Z test p < 0.05). The AUCs in the 18–39.9-years group were higher than that in the 40–59.9-years and 60–84-years groups (0.894 vs. 0.842 and 0.818 for SBP; 0.889 vs. 0.818 and 0.759 for DBP; Z test p < 0.05). The total optimal cut-off points of blood pressure for predicting arteriosclerosis were 123.5/73.5 mmHg (SBP/DBP) overall; 123.5/73.5 and 126.5/79.5 mmHg for women and men, respectively; and 120.5/73.5, 123.5/76.5, and 126.5/75.5 mmHg for 18–39.9-years, 40–59.9-years, and 60–84-years groups, respectively. Blood pressure indexes had a high predictive performance for identifying arteriosclerosis with the optimal cut-off point of 123.5/73.5 mmHg (SBP/DBP) in eastern Chinese adults. Women or the younger population have a higher predictive performance and lower cut-off points to identify arteriosclerosis.
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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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".