Effects of blood pressure lowering on cardiovascular events, in the context of regression to the mean
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical relevance of regression to the mean for clinical trials and clinical practice. METHODS: MEDLINE was searched until February 2018 for randomized trials of BP lowering with over 1000 patient-years follow-up per group. We estimated baseline mean BP, follow-up mean (usual) BP amongst patients grouped by 10 mmHg strata of baseline BP, and assessed effects of BP lowering on coronary heart disease (CHD) and stroke according to these BP levels. RESULTS: Eighty-six trials (349 488 participants), with mean follow-up of 3.7 years, were included. Most mean BP change was because of regression to the mean rather than treatment. At high baseline BP levels, even after rigorous hypertension diagnosis, downwards regression to the mean caused much of the fall in BP. At low baseline BP levels, upwards regression to the mean increased BP levels, even in treatment groups. Overall, a BP reduction of 6/3 mmHg lowered CHD by 14% (95% CI 11-17%) and stroke by 18% (15-22%), and these treatment effects occurred at follow-up BP levels much closer to the mean than baseline BP levels. In particular, more evidence was available in the SBP 130-139 mmHg range than any other range. Benefits were apparent in numerous high-risk patient groups with baseline mean SBP less than 140 mmHg. CONCLUSION: Clinical practice should focus less on pretreatment BP levels, which rarely predict future untreated BP levels or rule out capacity to benefit from BP lowering in high cardiovascular risk patients. Instead, focus should be on prompt, empirical treatment to maintain lower BP for those with high BP and/or high risk.
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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 | 0.037 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".