Reactive Hyperemia Index Associated With Atherosclerotic Cardiovascular Disease Under Treatment for Lifestyle Diseases
Bibliographic record
Abstract
BACKGROUND: Endothelial dysfunction is an early phase of atherosclerosis and causes atherosclerotic cardiovascular disease (ASCVD), but the cutoff reactive hyperemia index (RHI) for identifying ASCVD patients under treatment for lifestyle diseases is unknown. METHODS: Patients who visited Cardiology Section, Izumi General Medical Center, Kagoshima, Japan and were measured RHI using Endo-PAT 2000 during May 2014 and March 2016 were enrolled. We divided them into ASCVD and non-ASCVD groups and investigated the association with RHI between the groups. RESULTS: ASCVD group included 195 patients and non-ASCVD group included 288 patients. Age, body mass index and the rate of male, hypertension, diabetes, dyslipidemia and chronic kidney disease in the ASCVD group were significantly higher than those in non-ASCVD group. RHI in the ASCVD group (1.65 (1.40 - 1.92)) was significantly lower than that in the non-ASCVD group (1.73 (1.45 - 2.24)) (P < 0.05). In multi-regression analysis, RHI (odds ratio: 0.67, 95% confidence interval: 0.45 - 0.99, P = 0.04) was independently associated with ASCVD after adjusted by age, sex, body mass index, smoking, hypertension, diabetes, dyslipidemia and chronic kidney disease. The cutoff RHI value between the groups was 1.80. CONCLUSION: RHI was significantly associated with ASCVD under treatment for lifestyle diseases and the cutoff value was 1.80.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".