Automated ‘oscillometric’ blood pressure measuring devices: how they work and what they measure
Bibliographic record
Abstract
Automated 'oscillometric' blood pressure (BP) measuring devices (BPMDs) were developed in the 1970s to replace manual auscultatory BP measurement by mercury sphygmomanometer. Automated BPMDs that have passed accuracy testing versus a reference auscultatory sphygmomanometer using a scientifically accepted validation protocol are recommended for clinical use globally. Currently, there are many thousands of unique automated BPMDs manufactured by hundreds of companies, with each device using proprietary algorithms to estimate BP and using a method of operation that is largely unchanged since inception. Validated automated BPMDs provide similar BP values to those recorded using manual auscultation albeit with potential sources of error mostly associated with using empirical algorithms to derive BP from waveform pulsations. Much of the work to derive contemporary BP thresholds and treatment targets used to manage cardiovascular disease risk was obtained using automated BPMDs. While there is room for future refinement to improve accuracy for better individual risk stratification, validated BPMDs remain the recommended standard for office and out-of-office BP measurement to be used in hypertension diagnosis and management worldwide.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".