Predicting Out-of-Office Blood Pressure in the Clinic for the Diagnosis of Hypertension in Primary Care
Bibliographic record
Abstract
Clinical guidelines in the United States and United Kingdom recommend that individuals with suspected hypertension should have ambulatory blood pressure (BP) monitoring to confirm the diagnosis. This approach reduces misdiagnosis because of white coat hypertension but will not identify people with masked hypertension who may benefit from treatment. The Predicting Out-of-Office Blood Pressure (PROOF-BP) algorithm predicts masked and white coat hypertension based on patient characteristics and clinic BP, improving the accuracy of diagnosis while limiting subsequent ambulatory BP monitoring. This study assessed the cost-effectiveness of using this tool in diagnosing hypertension in primary care. A Markov cost-utility cohort model was developed to compare diagnostic strategies: the PROOF-BP approach, including those with clinic BP ≥130/80 mm Hg who receive ambulatory BP monitoring as guided by the algorithm, compared with current standard diagnostic strategies including those with clinic BP ≥140/90 mm Hg combined with further monitoring (ambulatory BP monitoring as reference, clinic, and home monitoring also assessed). The model adopted a lifetime horizon with a 3-month time cycle, taking a UK Health Service/Personal Social Services perspective. The PROOF-BP algorithm was cost-effective in screening all patients with clinic BP ≥130/80 mm Hg compared with current strategies that only screen those with clinic BP ≥140/90 mm Hg, provided healthcare providers were willing to pay up to £20 000 ($26 000)/quality-adjusted life year gained. Deterministic and probabilistic sensitivity analyses supported the base-case findings. The PROOF-BP algorithm seems to be cost-effective compared with the conventional BP diagnostic options in primary care. Its use in clinical practice is likely to lead to reduced cardiovascular disease, death, and disability.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".