Do We Need a New Definition of Hypertension After SPRINT?
Bibliographic record
Abstract
Hypertension has been defined by the levels of blood pressure (BP) above which lowering BP will reduce the cardiovascular risk associated with elevated BP. This level has been classically 140/90mm Hg on the basis of actuarial data from the insurance industry. However, we now know that cardiovascular risk rises progressively from levels as low as 115/75mm Hg upward with a doubling of the incidence of both coronary heart disease and stroke for every 20/10mm Hg increment of BP. 1 Accordingly, the concept of prehypertension was introduced for systolic BPs (SBPs) between 120 and 140mm Hg by the Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC7). 2 These levels of BP are associated with increased cardiovascular risk, 3 but there was until recently no evidence that lowering BP below 120mm Hg decreased cardiovascular risk further. Furthermore, there is little in the form of randomized clinical trials that demonstrates that in uncomplicated hypertension without cardiovascular risk factors or target organ damage, lowering SBP of <160mm Hg reduces cardiovascular risk. 4 However, this has been challenged by a recent systematic review and meta-analysis. 5
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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.019 | 0.041 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".