Office blood pressure measurement in the 21st century
Bibliographic record
Abstract
Measurement of blood pressure (BP) using the auscultatory method must follow specific rules and conditions to be reliable. Nonetheless, these requirements are often not followed in clinical practice, resulting in inaccurate BP readings. Simply replacing manual sphygmomanometers with an oscillometric device may still produce readings that are associated with a white coat effect. These limitations can be overcome by using an oscillometric sphygmomanometer that automatically records multiple readings with the patient resting quietly and alone, called automated office (AO)BP. AOBP produces office readings with a reduced white coat effect, which are also similar to the awake ambulatory BP. There is also evidence that AOBP is a better predictor of target organ damage than attended office BP. Furthermore, clinical outcome data support AOBP as having both a similar diagnostic threshold as awake ambulatory BP and a lower treatment target. Using AOBP in clinical practice simplifies recording office BP by not requiring an additional period of rest before activation of the device and by not having staff present during the actual measurements. Recent studies have reported that automatic BP measurements taken by staff in research studies with close adherence to guidelines using AOBP devices may produce similar readings to AOBP. Further research is needed to determine the best method for recording BP at systolic targets < 130 mm Hg and the relationship of office BP to ambulatory BP and home BP.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".