Automated office blood pressure – being alone and not location is what matters most
Bibliographic record
Abstract
OBJECTIVE: Measurement of office blood pressure using a fully automated sphygmomanometer that takes multiple readings with the patient resting quietly alone has been called automated office blood pressure (AOBP). Almost all AOBP research has involved the patient resting alone in an examining room, which is often impractical in a clinical setting. The possibility that valid AOBP readings can be obtained with the patient resting quietly in a waiting room was examined. METHODS: AOBP readings using the BpTRU device recorded with the patient resting quietly in the waiting room were obtained in patients referred for ambulatory BP monitoring. The relationship between the AOBP and the awake ambulatory blood pressure (AABP) (mmHg) was examined. RESULTS: In 422 patients, the mean (±SD) AABP (139.4±13.4/80.7±10.6) was similar to the mean AOBP recorded in the waiting room (140.5±19.8/83.1±11.2), with both values being significantly lower than a single office BP (155.1±18.7/90.2±12.7) taken by a nurse. In the 178 untreated patients, the mean systolic AOBP and AABP were almost identical, with the diastolic AOBP being 1.5 mmHg higher. Bland-Altman plots for systolic BP showed a relatively consistent relationship for AOBP versus the AABP over the range of BPs recorded. The sensitivity, specificity, and accuracy for AOBP versus AABP were comparable with the values obtained with AOBP recorded previously in an examining room. CONCLUSION: AOBP readings recorded in a waiting room are comparable with the AABP, making it possible to obtain AOBP in clinical practice without the need to occupy an examining room.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".