New approach to measurement of blood pressure in the office
Bibliographic record
Abstract
Automated office blood pressure (AOBP) measurement has important advantages over conventional manual office blood pressure (MOBP) readings. AOBP requires the use of a fully automated sphygmomanometer which takes multiple readings with the patient resting alone. By following these three principles of AOBP, it is possible to obtain office readings which are similar to home BP and to the awake ambulatory BP recorded with 24-h BP monitoring. All three methods of automated BP measurement define hypertension as a BP ≥ 135/85 mm Hg. The correlation between the awake ambulatory BP (a recognized gold standard for predicting future cardiac events in relation to BP status) and AOBP is significantly stronger than it is for routine MOBP. AOBP eliminates office-induced hypertension (white coat hypertension) and correlates with target organ damage significantly stronger than does routine MOBP. After 100 years of manual BP measurement with the mercury sphygmomanometer, it is now time to adopt AOBP for use in routine clinical practice in order to achieve a more accurate assessment of a patient's BP status and future cardiovascular risk. La medición automática de la presión sanguínea en consulta (MAPSC) presenta importantes ventajas sobre la medición manual de la presión arterial en consulta (MMPAC). La MAPSC requiere el uso de un esfigmomanómetro totalmente automático que registra múltiples lecturas con el paciente en reposo y sin la presencia del clínico. Al seguir estos tres principios de la MAPSC, es posible obtener lecturas en consulta similares a las obtenidas en domicilio y con monitorización ambulatoria de la PA de 24 horas con el paciente despierto. Los tres métodos de medición automática de la PA definen la hipertensión como una presión arterial ≥ 135/85 mmHg. La correlación entre la PA ambulatoria con el paciente despierto (un estándar de oro reconocido para la predicción de futuros eventos cardíacos en relación con el estado de la PA) y la MAPSC es mucho mayor que para la MMPAC rutinaria. La MAPSC elimina la hipertensión de bata blanca y se correlaciona con el daño al órgano diana de forma significativamente superior que la MMPAC rutinaria. Después de 100 años de medición manual de la PA con el esfigmomanómetro de mercurio, es hora de adoptar el uso de la MAPSC de forma rutinaria en la práctica clínica a fin de lograr una evaluación más precisa de la PA del paciente y su riesgo cardiovascular futuro.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".