Differences in heart stroke volume between Han and Korean-Chinese nationalities and correlative factors.
Bibliographic record
Abstract
OBJECTIVE: To compare the differences in stroke volume (SV) and stroke volume index (SI) between Han and Korean-Chinese and to investigate the correlated risk factors. METHODS: A total of 1 647 Han and 876 Korean-Chinese aged 10-80 years were investigated. SV, SI, cardiac output, cardiac output index, heart rate (HR), systemic vascular resistance (SVR), systemic vascular resistance index (SVRI), and blood pressure were measured. RESULTS: SV/SI values in Korean-Chinese were lower than those in the Han of the same sex and age. Covariance analysis showed that, apart from the effect of sex, age and body mass index (BMI), the differences in SV and SI between the two cohorts were still significant (P<0.001). Multiple regression analysis revealed that the SV difference between the two ethnicities was affected (in descending order from a strong to weak correlation) by SVR, SVRI, HR, diastolic blood pressure, mean arterial pressure, BMI, and systolic blood pressure, while the SI difference was affected by SVR, SVRI, HR, mean arterial pressure, diastolic and systolic blood pressure, and BMI. CONCLUSION: The Fact that SV and SI in Korean-Chinese are lower than those in Han is related with higher SVR, HR and blood pressure in the Korean-Chinese.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Comparison of cardiac stroke volume between ethnic groups; a clinical physiology question.
This compares cardiovascular measurements between population groups, not research itself.
Clinical physiology comparing stroke volume across ethnic groups; patient physiology, not research.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".