Statistical inference in experimental neuroscience is routinely detached from the experimental record: analytic choices are reported in prose summaries that do not expose the code, assumptions, or decision pathways that produced the results. This detachment limits reproducibility and impairs peer review. Here, we describe the Companion Statistical Report (CSR), a structured, versioned document format designed to accompany empirical neuroscience manuscripts as peer-reviewed as a peer-reviewed resource. The CSR integrates data provenance, preprocessing decisions, exploratory analyses, model specifications, assumption diagnostics, inference with effect sizes, and sensitivity analyses into a single executable document, authored in Quarto 1.8 and supporting both R and Python workflows. We provide an open template hosted at on GitHub that implements this format with institutional branding, parameterization, and version tracking. The template was developed by the Bertrand Russell Research Excellence Group (NEC) at the School of Medicine, Rio de Janeiro State University. By making analytic choices auditable and reproducible by design, CSRs are designed to reduce the gap between what neuroscience experiments measure and what published statistics claim, offering a tractable and immediately implementable step toward greater transparency.

Background/Objectives: Hypertensive disorders of pregnancy, particularly preeclampsia and eclampsia, are associated with high maternal and fetal morbidity and mortality, with maternal obesity representing a major, modifiable risk factor. This study aimed to estimate the association between obesity and preeclampsia/eclampsia (PEC) in obstetric hospitalizations in the state of São Paulo, Brazil, and to examine risk trends across maternal age groups and between two distinct temporal cohorts. Methods: This population-based observational study analyzed 2,338,934 obstetric hospitalizations using data from the Hospital Information System of the Unified Health System (SIH/SUS). PEC and obesity diagnoses were coded according to the International Classification of Diseases, 10th Revision (ICD-10). Logistic regression models were applied to estimate associations adjusted for maternal age, complemented by adjusted marginal predictions. Results: PEC occurred more than twice as often among women with obesity than without (5.4% vs. 2.2%; OR 2.13, 95% CI 1.84–2.47) and was associated with longer hospital stays (3.52 vs. 2.70 days) and higher costs (R$797.91 vs. R$602.69). The absolute risk difference between obese and non-obese women declined from 24.7% to 13.6% at age 20 between the 2017–2019 and 2023–2025 cohorts, while the relative risk gradient remained stable across all ages. Conclusions: Obesity is strongly associated with increased PEC risk, longer hospitalization, and higher costs. The observed attenuation in absolute risk differences in the most recent cohort reinforces the need for preventive interventions, nutritional surveillance, and public health policies aimed at reducing hypertensive complications during pregnancy for both obese and non-obese women.

Forest and funnel plots are the standard graphical outputs of evidence synthesis, yet the most widely used R functions draw them directly on the graphics device, which makes them hard to restyle, combine with other figures, or embed in a reproducible workflow. We present ggmeta, a ggplot2 extension that builds forest and funnel plots from meta package objects or from tidy data frames and returns them as ordinary ggplot objects, with each plot component implemented as a separate ggproto layer. The package handles twenty-one summary measures with measure-specific back-transformation, can pool effects from a data frame by the inverse-variance and DerSimonian–Laird methods, handles subgroups and table columns, and provides style presets modelled on JAMA, BMJ, and RevMan 5. We demonstrate ggmeta on five open datasets from published meta-analyses covering risk ratios, odds ratios, mean differences, Fisher-z correlations, and logit proportions. Plotted summaries matched the source objects exactly, the standalone pooling routine agreed with metafor and meta to machine precision, and the drawn coordinates matched the input values. A benchmark against five other R forest-plot tools reports code length, rendering time, and memory use. All code and data are distributed under the GPL (≥3) licence.