Comparison Between an Automated and Manual Sphygmomanometer in a Population Survey. Commentary
Bibliographic record
Abstract
BACKGROUND An automated sphygmomanometer, the BpTRU, was used in a blood pressure (BP) survey of 2,551 residents in the province of Ontario. Automated BP readings were compared with measurements taken by a mercury sphygmomanometer under standardized conditions in a random 10% sample. METHODS BP was recorded in 238 individuals in random order using both a standard mercury device and an automated BP recorder, the BpTRU. All subjects rested for 5 min prior to the first BP reading, which was then discarded. The mean of the next three readings was obtained using the mercury device whereas the BpTRU was set to record a mean of five readings taken at 1 min intervals with subjects resting alone in a quiet room. RESULTS The mean s.d. BP with the automated device was 115 ± 16/71 ± 10mm Hg compared to 118 ± 16/74 ± 10 mm Hg for the manual BP (P < 0.001). A systolic BP ≥140 mm Hg was present for 16 automated and 19 manual readings. Similarly, the diastolic BP was ≥90 mm Hg for 9 automated and 14 manual readings. Linear regression analysis showed that automated BP was a significant (P < 0.001) predictor of both manual systolic and diastolic BP. CONCLUSION Conventional manual BP readings can be replaced by readings taken using a validated, automated BP recorder in population surveys. The slightly lower readings obtained with the BpTRU device (in the context of reduced observer-subject interaction) may be a more accurate estimate of BP status.
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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 | 0.013 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".