Validation of a new algorithm for the BPM-100 electronic oscillometric office blood pressure monitor
Bibliographic record
Abstract
BACKGROUND: To test the accuracy of a new algorithm for the BPM-100, an automated oscillometric blood pressure (BP) monitor, using stored data from an independently conducted validation trial comparing the BPM-100(Beta) with a mercury sphygmomanometer. DESIGN: Raw pulse wave and cuff pressure data were stored electronically using embedded software in the BPM-100(Beta), during the validation trial. The 391 sets of measurements were separated objectively into two subsets. A subset of 136 measurements was used to develop a new algorithm to enhance the accuracy of the device when reading higher systolic pressures. The larger subset of 255 measurements (three readings for 85 subjects) was used as test data to validate the accuracy of the new algorithm. METHODS: Differences between the new algorithm BPM-100 and the reference (mean of two observers) were determined and expressed as the mean difference +/- SD, plus the percentage of measurements within 5, 10, and 15 mmHg. RESULTS: The mean difference between the BPM-100 and reference systolic BP was -0.16 +/- 5.13 mmHg, with 73.7% < or = 5 mmHg, 94.9% < or = 10 mmHg and 98.8% < or = 15 mmHg. The mean difference between the BPM-100 and reference diastolic BP was -1.41 +/- 4.67 mmHg, with 78.4% < or = 5 mmHg, 92.5% < or = 10 mmHg, and 99.2% < or = 15 mmHg. These data improve upon that of the BPM-100(Beta) and pass the AAMI standard, and 'A' grade BHS protocol. CONCLUSION: This study illustrates a new method for developing and testing a change in an algorithm for an oscillometric BP monitor utilizing collected and stored electronic data and demonstrates that the new algorithm meets the AAMI standard and BHS protocol.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".