May Measurement Month 2019
Bibliographic record
Abstract
Elevated blood pressure remains the single biggest risk factor contributing to the global burden of disease and mortality. May Measurement Month is an annual global screening campaign aiming to improve awareness of blood pressure at the individual and population level. Adults (≥18 years) recruited through opportunistic sampling were screened at sites in 92 countries during May 2019. Ideally, 3 blood pressure readings were measured for each participant, and data on lifestyle factors and comorbidities were collected. Hypertension was defined as a systolic blood pressure ≥140 mm Hg, or a diastolic blood pressure ≥90 mm Hg (mean of the second and third readings) or taking antihypertensive medication. When necessary, multiple imputation was used to estimate participants' mean blood pressure. Mixed-effects models were used to evaluate associations between blood pressure and participant characteristics. Of 1 508 130 screenees 482 273 (32.0%) had never had a blood pressure measurement before and 513 337 (34.0%) had hypertension, of whom 58.7% were aware, and 54.7% were on antihypertensive medication. Of those on medication, 57.8% were controlled to <140/90 mm Hg, and 28.9% to <130/80 mm Hg. Of all those with hypertension, 31.7% were controlled to <140/90 mm Hg, and 350 825 (23.3%) participants had untreated or inadequately treated hypertension. Of those taking antihypertensive medication, half were taking only a single drug, and 25% reported using aspirin inappropriately. This survey is the largest ever synchronized and standardized contemporary compilation of global blood pressure data. This campaign is needed as a temporary substitute for systematic blood pressure screening in many countries worldwide.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.835 | 0.701 |
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".