Abstract P111: Comparison Of Automated Office Blood Pressure And Self-Measured Blood Pressure: Results From An Ongoing Self-Measured Blood Pressure Program
Bibliographic record
Abstract
Background: HTN treatment based on AOBP lowers risk of CVD. SMBP has been promoted as an alternative to AOBP that could improve access and achieve more timely BP control. However, questions remain about the concordance between SMBP and AOBP. Objective: To compare SMBP and AOBP measurements in a 6-month SMBP program. Methods: In this quality improvement study, we examined data from an ongoing SMBP pilot program in a large general medicine practice in Boston. Referred patients underwent AOBP during an initialization clinic visit and then performed SMBP for a week each month over a 6 month period. During the 6 th month of the program, patients performed a final week of SMBP and returned to clinic for an AOBP assessment. We compared the 6 th month mean BPs via paired t-tests. Results: Of 129 patients referred, 74 underwent an initialization visit and at least 1 month of monitoring (mean age 59±13, 53% female, 28% Black). The mean referral BP was 140±11/84±11 mm Hg. In contrast, during program initialization, mean AOBP was 131±13/79±10 (difference compared to referral BP was -9/-5 mm Hg; P <0.001 for both). The mean BP measured the month after initialization using the 1-week SMBP protocol was 133±12/83±7 (difference compared to AOBP was +2/+4; P =0.18 for SBP; P <0.001 for DBP). For the 12 patients that completed 6-months thus far, mean AOBP at the 6-month visit was 127±10/76±8. The mean BP the week of SMBP during the 6 th month of the program was 124±9/79±5 (difference compared to 6-month AOBP was -3/+2 mm Hg; P >0.05 for both) ( Figure ). Conclusion: BP measures from SMBP tracked well with in-office AOBP measurements. These findings add to existing evidence that SMBP might be used in lieu of AOBP for HTN management.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".