Bibliographic record
Abstract
According to the largest survey of its kind to date, since 1990, the number of 30-to 79-year-olds with hypertension has increased rapidly from 650 million to 1.28 billion. Although this number is rising rapidly, no one knows what causes blood pressure to rise. The cause of hypertension is still unclear. The factors that contribute to elevated blood pressure are numerous and confusing. Studies have shown that abnormal biological rhythms can lead to hypertension. One of the key factors contributing to elevated blood pressure is an abnormal biorhythm. It has also been shown that abnormal biological rhythms can lead to hypertension. The nervous system, the renin-angiotensin-aldosterone pathway and melatonin secretion from the pineal gland may all be affected by circadian rhythm problems, which in turn affects glucocorticoid-mediated hypertension, and thus the development and progression of hypertension. The current intervention of it mainly based on pharmacological therapy, supplemented by improvement of lifestyle habits. Several new strategies for the treatment of hypertension have emerged in recent years. For example, acupuncture needle treatment, melatonin promotion treatment, RDN treatment hair etc. This study will present these methods and experiments to draw conclusions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".