Impact of choosing the closest rather than the mean auscultatory blood pressure value on the European Society of Hypertension International Protocol device validation results
Bibliographic record
Abstract
OBJECTIVE: When calculating the difference between alternating auscultatory reference standard (ARS) and the automated device-under-test (DUT) blood pressure (BP) measurements, the European Society of Hypertension International Protocol (ESH-IP) allows investigators to choose the closest ARS value to the DUT value, rather than using the mean of the ARS readings [which is mandated by the International Organization for Standardization (ISO)]. The impact of this rule on ESH-IP validation study results is unknown and was assessed. DESIGN AND METHODS: Nine alternating BP measurements performed according to the ISO protocol were obtained in 94 subjects. The impact of using the closest rather than the mean ARS reading on mean error, SD of the difference, and proportion of readings with DUT-ARS differences within 5, 10, and 15 mmHg was determined. RESULTS: Mean age was 58.6 ± 18.3 years, screening BP was 126.4/77.7 mmHg, and arm circumference was 32.0 ± 4.7 cm. DUT-ARS difference was 0.0 ± 5.3/-0.5 ± 5.0 mmHg using the closest ARS and -0.2 ± 6.5/-0.7 ± 5.9 mmHg using the mean ARS. When using the closest rather than the mean ARS value, the proportion of systolic readings with absolute DUT-ARS differences ≤5 mmHg was 73% (vs. 60% for the mean ARS method), ≤10 mmHg was 93% (vs. 88%), and ≤15 mmHg was 99% (vs. 98%). Corresponding values for diastolic BP were 73% (vs. 62%) for differences ≤5 mmHg, 94% (vs. 91%) for ≤10 mmHg, and 99% (vs. 99%) for ≤15 mmHg. CONCLUSION: Using the closest rather than the mean ARS value results in more favourable validation study results and increases likelihood of passing.
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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 | 0.308 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".