Predicting out-of-office blood pressure in a diverse us population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 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 it