Self-measurement of blood pressure: accuracy, patient preparation for readings, technique and equipment
Bibliographic record
Abstract
OBJECTIVE: Self-measurement of blood pressure is commonly performed by those persons with hypertension and is advocated in many national hypertension guidelines. We examined accuracy of readings, patient knowledge, and preparation for readings, technique and equipment. DESIGN: The study was a prospective observational design. Sixty-nine hypertensive patients were recruited from a tertiary referral center and by newspaper advertisement. All patients had previously self-measured their blood pressure. The patients initially measured their blood pressure under direct supervision in a clinic using their usual preparation, technique and their own equipment. Then after a five-min rest, blood pressures were measured twice both by research nurse and the patient in an alternating sequence. The nurse used a standardized blood pressure measurement technique. RESULTS: Inadequate patient knowledge and performance of measurement technique and inaccurate equipment was common. The average initial patient systolic reading prior to the five-minute rest was higher than that of the trained nurse (9.1 +/- 13 mmHg systolic, p < 0.001 and 1.5 +/- 8.0 mmHg diastolic, p = 0.12). Almost half (42%) of the initial patient blood pressure readings differed in classification of hypertension/normotension from the nurse. The difference between the patient and nurse readings after the five-min rest was 3.8 +/- 11.8 / 1.1 +/- 6.8 mmHg. CONCLUSIONS: Care must be taken in interpreting patient self-measured blood pressure unless there has been adequate training and assessment of patient and equipment accuracy. Studies of health care professionals reveal similar problems therefore widespread efforts to standardize blood pressure measurement are necessary.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".