Developing population-based hypertension control programs
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
Hypertension remains the leading cause of cardiovascular disease globally despite the availability of safe and effective treatments. Unfortunately, many barriers exist to controlling hypertension, including a lack of effective screening and awareness, an inability to access treatment and challenges with its management when it is treated. Addressing these barriers is complex and requires engaging in a systematic and sustained approach across communities over time. This analysis aims to describe the key elements needed to create an effective delivery system for hypertension control. A successful system requires political will and supportive leadership at all levels of an organization, including at the point of care delivery (office or clinic), in the health care system, and at regional, state and national levels. Effective screening and outreach systems are necessary to identify individuals not previously diagnosed with hypertension, and a system for follow up and tracking is needed after people are diagnosed. Implementing simple protocols for treating hypertension can reduce confusion among providers and increase treatment efficiency. Ensuring easy access to safe, effective and affordable medications can increase blood pressure control and potentially decrease health care system costs. Task-sharing among members of the health care team can expand the services that are delivered. Finally, monitoring of and reporting on the performance of the health care team are needed to learn from those who are doing well, disseminate ideas to those in need of improvement and identify individual patients who need outreach or additional care. Successful large-scale hypertension programs in different settings share many of these key elements and serve as examples to improve systems of hypertension care delivery throughout the world.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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