Scaling up effective treatment of hypertension—A pathfinder for universal health coverage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High blood pressure is the world's leading cause of death, but despite treatment for hypertension being safe, effective, and low cost, most people with hypertension worldwide do not have it controlled. This article summarizes lessons learned in the first 2 years of the Resolve to Save Lives (RTSL) hypertension management program, operated in coordination with the World Health Organization (WHO) and other partners. Better diagnosis, treatment, and continuity of care are all needed to improve control rates, and five necessary components have been recommended by RTSL, WHO and other partners as being essential for a successful hypertension control program. Several hurdles to hypertension control have been identified, with most related to limitations in the health care system rather than to patient behavior. Treatment according to standardized protocols should be started as soon as hypertension is diagnosed, and medical practices and health systems must closely monitor patient progress and system performance. Improvement in hypertension management and control, along with elimination of artificial trans fat and reduction of dietary sodium consumption, will improve many aspects of primary care, contribute to goals for universal health coverage, and could save 100 million lives worldwide over the next 30 years.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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) | 0.000 | 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 it