Unraveling the link between birth order and blood pressure: insights from a comprehensive systematic review and meta-analysis
Bibliographic record
Abstract
The objective of the present systematic review was to incorporate previous studies investigating the association of birth order with the risk of systolic and diastolic blood pressure (DBP). We employed random-effects and Bayesian meta-analyses, complemented by subgroup and sensitivity analyses, including funnel plots, Begg's rank correlation test, Egger's linear regression test, Galbraith plots, and leave-one-out meta-analysis. Of the 13 articles analyzed, 92% (12 articles) were published from 2010 onwards. The aggregate sample comprised 466 853 firstborns and 646 786 later-born individuals. Geographically, the studies were primarily conducted in Europe (54%), followed by Asia (23%), and America (23%). The pooled mean difference for systolic blood pressure (SBP) under a random-effects model was 0.28 mm Hg (95% CI: −7.03 to 7.59), and for DBP was 0.33 mm Hg (95% CI: −5.38 to 6.04), neither of which reached statistical significance (SBP: Z = 0.08, P = 0.939; DBP: Z = 0.11, P = 0.910). Sensitivity analyses supported these findings. Bayesian meta-analysis presented a 95% credible interval for SBP and DBP ranging from −7.25 to 7.84 and −5.60 to 6.27, respectively. The investigation found no substantial evidence of a significant difference in SBP and DBP between firstborns and later-born individuals, challenging the hypothesis that birth order significantly impacts blood pressure levels. ID registration number: 567971.
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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 | 0.036 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".