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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".