[OP.6B.04] INDIVIDUAL PATIENT DATA META-ANALYSIS OF SELF-MONITORING OF BLOOD PRESSURE (BP-SMART)
Bibliographic record
Abstract
Objective: Summary meta-analyses suggest that self-monitoring of blood pressure reduces blood pressure in hypertension but important questions remain regarding how best to utilise it and for which groups self-monitoring might be most appropriate. An Individual Patient Data meta-analysis aimed to investigate this further. Design and method: A systematic review captured randomized trials which compared hypertensive patients who self-monitored BP with those who did not. Individual patient data (IPD) were requested from all eligible studies and entered into a 2 stage meta-analysis stratified by trial and adjusting for age, sex, diabetes, baseline BP and intensity of intervention. The primary outcome was change in clinic BP at 12 months. Subgroup analyses assessed the impact of age, sex, baseline BP, baseline treatment and co-morbidities. Results: Of 2,508 articles in the initial search, 30 trials were eligible, 23 reported the primary outcome. Individual patient data were available from 21 trials, including one unpublished that had not appeared in the search (8,931 participants). Self-monitoring was associated with reduced clinic systolic blood pressure compared to usual care (−3×3 mmHg, [95% CI −5×0, −1×5 mmHg] at 12 months). Systolic blood pressure reduction and control to target increased with intensity of co-intervention (ranging from no additional support to intensive support). Similar results were seen for diastolic blood pressure. Few data were available after 12 months. Self-monitoring was most effective in those with fewer antihypertensive medications and higher baseline systolic blood pressure up to 170 mmHg but there was no effect in people with previous stroke. Conclusions: Self-monitoring of blood pressure leads to clinically significant blood pressure reduction when combined with more intensive co-interventions including systematic medication titration, education or lifestyle counselling which persists for at least 12 months. People with resistant hypertension or previous stroke may not benefit, perhaps reflecting maximal treatment.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.165 | 0.016 |
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".