Optimizing observer performance of clinic blood pressure measurement
Bibliographic record
Abstract
: High blood pressure (BP) is a highly prevalent modifiable cause of cardiovascular disease, stroke, and death. Accurate BP measurement is critical, given that a 5-mmHg measurement error may lead to incorrect hypertension status classification in 84 million individuals worldwide. This position statement summarizes procedures for optimizing observer performance in clinic BP measurement, with special attention given to low-to-middle-income settings, where resource limitations, heavy workloads, time constraints, and lack of electrical power make measurement more challenging. Many measurement errors can be minimized by appropriate patient preparation and standardized techniques. Validated semi-automated/automated upper arm cuff devices should be used instead of auscultation to simplify measurement and prevent observer error. Task sharing, creating a dedicated measurement workstation, and using semi-automated or solar-charged devices may help. Ensuring observer training, and periodic re-training, is critical. Low-cost, easily accessible certification programs should be considered to facilitate best BP measurement practice.
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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 | 0.079 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".