Publication bias is a fundamental threat to the validity of systematic reviews and meta-analyses in clinical medicine. Yet current practice often reduces its assessment to the mechanical application of funnel plots, asymmetry tests, or single adjustment procedures, with limited attention to the underlying assumptions, alternative explanations, or implications for evidence certainty. This narrative methodological article reframes publication bias assessment as an interpretive and editorial responsibility rather than a purely technical problem. We examine what commonly used methods can and cannot reliably support. Detection tools function as nonspecific stress tests that identify deviations from simplified models; they do not diagnose selective publication but highlight situations in which the underlying assumptions require closer inspection. Adjustment approaches, including trim-and-fill, selection models, and regression-based methods, generate hypothetical estimates under unverifiable assumptions**, and therefore provide** sensitivity analyses rather than corrections that recover the true underlying effect. Divergence across adjustment methods is particularly informative, signaling inferential fragility rather than analytical failure. We identify five recurring misinterpretations encountered in peer review: equating asymmetry with proof of publication bias; privileging bias-adjusted estimates as inherently more credible; relying on a single adjustment method without examining assumption dependence; ignoring the plausibility of adjustment direction and magnitude; and overlooking implications for certainty of evidence. Editors and reviewers should prioritize transparency of assumptions, seriously consider alternative explanations, and calibrate conclusions proportionately. Viewing publication bias assessment as an interpretive responsibility rather than a methodological checklist promotes more disciplined inference and strengthens trust in clinical evidence synthesis.

Intravenous intralipid is widely used for women with recurrent reproductive failure —including recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and repeated IVF failure—despite limited evidence. This systematic review evaluated the effectiveness and safety of intralipid infusion in this population. We searched seven electronic databases from inception to April 14, 2025. RCTs and non-randomized comparative studies (NRCSs) were included for effectiveness analysis; case series and reports for safety. Two reviewers independently screened and extracted data. Risk of bias was assessed using RoB 2.0 and RoBANS 2.0. Meta-analyses used a random-effects model (risk ratios [RR] with 95% CI). Evidence certainty was assessed using GRADE. Seventeen studies were included (8 effectiveness, 11 safety). Pooled RCT data showed intralipid significantly improved clinical pregnancy rate vs. no treatment (RR 2.31, 95% CI 1.42–3.74; I² = 0%; GRADE certainty: low); this effect was not observed in the pooled NRS data for live birth rate or clinical pregnancy rate, and one large NRCS reported a significantly higher miscarriage rate in the intralipid group (RR 1.12, 95% CI 1.04–1.20). No significant differences were observed vs. IVIG or steroids. Serious adverse events were rare. GRADE certainty was very low to low across all outcomes. Current evidence does not support the routine use of intralipid in women with recurrent reproductive failure. While pooled RCT data suggest a potential benefit in clinical pregnancy rate, evidence certainty is low, and this benefit was not observed in the pooled NRS data. Adequately powered RCTs with standardized protocols and live birth rate as the primary endpoint are needed.

Systematic reviews and meta-analyses are pivotal for evidence-based decision-making but depend on the availability of precise statistical data. Researchers often encounter studies where essential statistics are missing or presented only in graphs, leading to potential data exclusion and selection bias. This study aims to provide specific methodologies for extracting or reconstructing the statistical parameters required for meta-analysis—specifically effect sizes (MD, OR, RR, HR) and their corresponding variance measures (SD, SE, variance)—from incomplete or graphically reported data. We describe calculation and extraction protocols for five specific scenarios encountered in medical literature: (1) continuous data missing standard deviations; (2) categorical data missing standard errors; (3) calculating risk estimates from frequency tables; (4) extracting continuous data presented solely in graphs; and (5) reconstructing hazard ratios from Kaplan-Meier survival curves. Valid meta-analysis requires both an effect size and a measure of variance. When these are not explicitly reported, they can often be derived from other available statistics or digital extraction from figures. While heterogeneity is inherent in meta-analysis, the methodology allows for error adjustment and robust synthesis. Therefore, preventing data loss via these extraction methods is preferable to excluding studies. Maximizing data inclusion enhances the comprehensive value and statistical power of the final analysis.

This paper focuses on basic meta-analyses using the updated RevMan Web version, based on the Cochrane Handbook of Systematic Reviews of Interventions for clinical trials. Theoretical statistical knowledge, such as the REML method for estimating heterogeneity variance in random-effects meta-analyses, the HKSJ method for reflecting the uncertainty of pooled estimates, and the prediction interval in a random-effects model for exploring true treatment effects in a future trial, is briefly described. Examples with synthetic data are presented to help with the understanding of meta-analysts.