Standards for the Uniform Reporting of Hypertension in Adults Using Population Survey Data: Recommendations From the World Hypertension League Expert Committee
Bibliographic record
Abstract
Surveillance and monitoring of cardiovascular risk factors including raised blood pressure are critical to informing efforts to prevent and control cardiovascular disease. Yet, many countries lack the capacity for adequate national surveillance. Furthermore, hypertension indicators are often reported in different ways, which hampers the ability to compare and assess progress. In order to encourage standardized hypertension surveillance reporting, the World Hypertension League assembled an Expert Committee to develop a standard set of core indicators, definitions, and recommended analyses. The recommended core indicators are: (1) blood pressure distribution, (2) prevalence of hypertension, (3) awareness of the condition, (4) antihypertensive drug treatment, and (5) control of hypertension based on drug therapy. Each of these can be reported overall and by age group and sex, with crude and age-standardized changes tracked over time in order to assess the impact of instituted policies and programs for hypertension prevention and control. An expanded list of indicators can also facilitate tracking of hypertension prevention and control efforts. Widespread adoption of these indicators and analyses could benefit all those conducting and analyzing hypertension surveys and will facilitate hypertension surveillance efforts.
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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 | 0.414 | 0.446 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.018 | 0.007 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".