Response to the Letter to the Editor on “Antecedent rest may not be necessary for automated office blood pressure at lower treatment targets”
Bibliographic record
Abstract
To a great extent, the recent ACC/AHA guidelines1 for target blood pressure (BP) of <130/80 mmHg were based upon the findings in the Systolic Blood Pressure Intervention Trial (SPRINT), in which most patients had office BP recorded using the Omron 907 with 5 minutes of antecedent rest.2 Using this technique in SPRINT, office BP at target (120/66 mm Hg) was 7/6 mmHg lower than the corresponding awake ambulatory BP.3 The results of our study4 suggested that the Omron 907 without any antecedent rest should produce readings which more closely approximate the awake ambulatory BP, when systolic BP is in the target range of 120-130 mmHg. Dr Padwal5 seems concerned about the variability of automated office (AO) BP. However, in our study, the lower readings taken after 5 minutes vs 0 minutes of rest accounted for much of the apparent variability. In reality, AOBP, when recorded in different settings with the same rest period, is highly consistent, with an intraclass correlation coefficient for systolic/diastolic BP of r = 0.90/r = 0.87.6 This high level of agreement for office BP has never been reported for manual BP readings in routine clinical practice. Dr Padwal's pejorative criticism5 of using the Omron 907 without antecedent rest misses the main point of our study. At a target systolic BP <130 mm Hg, office BP is less than the awake ambulatory BP, regardless of how it is measured.7 White coat effect is not common when BP is normal. Therefore, we should avoid using any method for office BP which produces excessively low readings. It is not necessary to know the office BP or home BP reading in advance, as maintained by Dr Padwal, in order to use the Omron 907 at target BP with the zero-minute rest setting. If the AOBP is high when taken without antecedent rest, the reading can always be repeated with the 5-minute rest setting. Home BP may be useful for other reasons. Since office BP, regardless of how it is measured, may differ from out-of-office readings, at some point it is important to perform 24-hour ambulatory BP monitoring or home BP with verifiable readings, in order to be certain that target BP has been achieved with drug therapy. It is premature to suggest that we should now abandon office BP in favor of monitoring patients for response to drug therapy only with home BP. No conflict of interests to declare
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.028 | 0.030 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".