A proposed algorithm for diagnosing hypertension using automated office blood pressure measurement
Bibliographic record
Abstract
OBJECTIVE: To validate an algorithm for the interpretation of automated office blood pressure (AOBP) measurement based upon data from untreated patients referred by physicians in the community for 24-h ambulatory blood pressure monitoring (ABPM). METHODS: An algorithm for interpreting AOBP readings was developed taking into account the previously documented equivalence of AOBP and mean awake ambulatory BP (ABP; mmHg), which were each classified as optimum BP (<130/80), borderline BP (130-139/80-89) and hypertension (>or=140/90). This classification was applied to data derived from 254 untreated patients undergoing 24-h ABPM, AOBP and routine manual BP taken at the patient's own family physician's office. RESULTS: The mean awake ABP (135.3 +/- 12.4/81.0 +/- 10.2) was similar to the mean AOBP (132.6 +/- 17.4/80.0 +/- 11.1) with both values being significantly (P < 0.001) lower than the routine manual BP (149.7 +/- 15.2/89.3 +/- 9.5). Of the 69 patients with a systolic AOBP at least 140, only five (7.3%) exhibited white-coat hypertension with a normal mean awake ambulatory systolic BP less than 130. Similarly, of the 47 patients with a diastolic AOBP at least 90, none had optimum BP (diastolic BP < 80 mmHg on ABPM). White-coat hypertension was significantly (P = 0.005/P = 0.006) more prevalent for systolic/diastolic BP (22.1%/13.4%) when routine, manual BP readings were analysed. CONCLUSION: In contrast to routine manual office BP, a diagnosis of hypertension by AOBP is unlikely to be associated with an optimum awake ABP.
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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 | 0.001 |
| 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".