High Blood Pressure 2016: Why Prevention and Control Are Urgent and Important. The World Hypertension League, International Society of Hypertension, World Stroke Organization, International Diabetes Foundation, International Council of Cardiovascular Prevention and Rehabilitation, International Society of Nephrology
Bibliographic record
Abstract
Increased blood pressure (BP) is the second leading risk factor for death and disability globally according to the Global Burden of Disease Study.1 This work is an updated version of the World Hypertension League (WHL) 2014 Hypertension Fact sheet2 (www.whleague.org). Dr Mark Niebylski and Kimbree Redburn are paid WHL consultants but report no other conflicts. Dr Ji-Guang Wang reports receiving research grants and consulting fees from several BP-measuring device companies including A&D, AVITA, Honsun, Omron, and Rossmax but reports no other conflicts. Dr Michael Weber is a consultant for and received travel support from Omron but reports no other conflicts. All other primary authors including those from WHL and the International Society of Hypertension report no conflicts of interest.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".