Letter to the Editor on “Antecedent rest may not be necessary for automated office blood pressure at lower treatment targets”
Bibliographic record
Abstract
In a comparison of automated office blood pressure (BP) measurements taken with and without five minutes of rest, Colella and colleagues conclude that “when readings in treated patients decrease to a systolic BP <130 mm Hg, the Omron 907XL may be used without any antecedent rest. Doing so would reduce the time required to record the office BP…”.1 How to implement this suggestion in clinical practice is unclear, because it would necessitate knowing the BP level prior to performing the measurement! Are the authors recommending that clinicians base this decision on prior automated office BP levels, perhaps from prior visits? If so, can they comment on the substantial BP variability found in their study? Despite performing research quality measurements, using the same device and measurement modality for all measurements, and employing a randomized cross-over design, their reported limits of agreement spanned nearly 30 mm Hg. Variability would be expected to be even higher in a “real world setting.” This would seem to preclude in most patients use of prior measurements for the purposes of determining if a patient should rest. I suppose one could use out-of-office measurements to predict the need for a rest period. However, given the superiority of out-of-office over in-office BP measurements,2, 3 one could simply eliminate the entire in-office measurement procedure altogether if high-quality out-of-office measurements were available. In fact, this would be the most easily implementable method of “minimizing” the time required for in-office BP measurement. Raj Padwal is a o-Founder of a BP measurement start-up company, mm Hg Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".