Bibliographic record
Abstract
Clinical practice guidelines have traditionally recommended manual blood pressure (BP) measurement in the office setting as the standard method for diagnosing hypertension. In reality, manual BP in routine clinical practice is relatively inaccurate, over-diagnoses hypertension by provoking office-induced increases in BP and correlates poorly with both the awake ambulatory BP and target organ damage. The most recent guidelines recommend 24-h ambulatory BP and home BP for diagnosing hypertension. The advent of automated office BP (AOBP) represents a third alternative to conventional manual BP measurement, one that maintains the role of office BP readings in the diagnosis and management of hypertension. AOBP has three basic principles: multiple readings taken using a fully automated sphygmomanometer with the patient resting quietly alone. AOBP eliminates office-induced hypertension such that the cut-point for a normal AOBP is the same as for the awake ambulatory BP and home BP. As compared to routine manual office BP, AOBP provides more accurate BP readings, is more consistent during repeated office visits and in different settings and correlates better with both the awake ambulatory BP and target organ damage. The advantages of AOBP over manual BP measurement support its use in 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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.012 |
| 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".