Bibliographic record
Abstract
It is now more than 30 years since Sir George Pickering addressed the question: “Is there a dividing line between normal and raised arterial pressure?”1 He concluded that the relationship between blood pressure and mortality was linear and that any separation between “normotension” and “hypertension” was arbitrary. In paying homage to these concepts, the recent JNC VI report2 has developed a gradient for normal office blood pressure by dividing it into several categories with higher readings related to cardiovascular risk. No doubt, Sir George would very much have approved these developments. Any attempt to define a normal home blood pressure must incorporate what we have learned from the office reading. For a start, the office blood pressure is generally recognized as a relatively imprecise measure of an individual’s blood pressure status, although it may provide reliable demographic information. Office readings are also subject to observer bias and may not represent an individual’s usual blood pressure such as when a white coat effect is present. Self-measurement of blood pressure does offer an alternative to the office reading. However, self-measured readings must be reliable and predictive of outcome before they can be recommended for routine clinical practice.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.038 | 0.047 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".