Predicting out-of-office blood pressure in a diverse us population
Bibliographic record
Abstract
Background\nThe PRedicting Out-of-OFfice Blood Pressure (PROOF-BP) algorithm accurately predicted out-of-office blood pressure (BP) among adults with suspected high BP in the UK and Canada. We tested the accuracy of PROOF-BP in a diverse US population and evaluated a newly developed US-specific algorithm (PROOF-BP-US).\n\nMethods\nAdults with ≥2 office BP readings and ≥10 awake BP readings on 24-hour ambulatory BP monitoring from four pooled US studies were included. We compared mean awake BP with predicted out-of-office BP using PROOF-BP and PROOF-BP-US. Our primary outcomes were hypertensive out-of-office systolic BP ≥130 mmHg and diastolic BP ≥80 mmHg.\n\nResults\nWe included 3,058 adults, mean (SD) age was 52.0 (11.9) years, 38% were male, and 54% were Black. The area under the receiver-operator characteristic curve (95% CI) for hypertensive out-of-office systolic BP was 0.81 (0.79-0.82) and diastolic BP was 0.76 (0.74-0.78) for PROOF-BP. For PROOF-BP-US, the area under the receiver-operator characteristic curve for hypertensive out-of-office systolic BP was 0.82 (0.81-0.83) and for diastolic BP was 0.81 (0.79-0.83). The optimal predicted out-of-office BP ranges for out-of-office BP measurement referral were 120-134/75-84 mmHg for PROOF-BP and 125-134/75-84 mmHg for PROOF-BP-US. The 2017 American College of Cardiology/American Heart Association BP guideline (referral range 130-159/80-99 mmHg) would refer 93.1% of adults not taking antihypertensive medications with office BP ≥130/80 mmHg in the National Health and Nutrition Examination Survey for out-of-office BP measurement, compared with 53.1% using PROOF-BP and 46.8% using PROOF-BP-US.\n\nConclusions\nPROOF-BP and PROOF-BP-US accurately predicted out-of-office hypertension in a diverse sample of US adults.\n\n
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".