Monitoring Initial Response to Angiotensin-Converting Enzyme Inhibitor–Based Regimens
Bibliographic record
Abstract
Most clinicians monitor blood pressure to estimate a patient's response to blood pressure-lowering therapy. However, the apparent change may not actually reflect the effect of the treatment, because a person's blood pressure varies considerably even without the administration of drug therapy. We estimated random background within-person variation, apparent between-person variation, and true between-person variation in blood pressure response to angiotensin-converting enzyme inhibitors after 3 months. We used meta-analytic mixed models to analyze individual patient data from 28 281 participants in 7 randomized, controlled trials from the Blood Pressure Lowering Trialists Collaboration. The apparent between-person variation in response was large, with SDs for change in systolic blood pressure/diastolic blood pressure of 15.2/8.5 mm Hg. Within-person variation was also large, with SDs for change in systolic blood pressure/diastolic blood pressure of 14.9/8.45 mm Hg. The true between-person variation in response was small, with SDs for change in systolic blood pressure/diastolic blood pressure of 2.6/1.0 mm Hg. The proportion of the apparent between-person variation in response that was attributed to true between-person variation was only 3% for systolic blood pressure and 1% for diastolic blood pressure. In conclusion, most of the apparent variation in response is not because of true variation but is a consequence of background within-person fluctuation in day-to-day blood pressure levels. Instead of monitoring an individual's blood pressure response, a better approach may be to simply assume the mean treatment effect.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".