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This systematic review and meta-analysis were conducted in strict accordance with the PRISMA 2020 guidelines. As this study utilizes secondary data from previously published trials, institutional review board (IRB) approval was not required; however, all included original studies adhered to ethical standards involving human subjects. The protocol was prospectively registered with the PROSPERO database immediately upon study initiation (Registration number: CRD42025637800).
Search Strategy and Selection Criteria Two independent researchers performed a comprehensive systematic search for Randomized Controlled Trials (RCTs) investigating Buyang Huanwu Decoction (BYHWD) for spinal cord injury (SCI). The search spanned from the inception of each database to January 2025. Electronic databases included PubMed, Embase, The Cochrane Library, Web of Science, CNKI, VIP, Wanfang, and CBM. The search strategy utilized a combination of controlled vocabulary (e.g., MeSH Terms in PubMed, Emtree in Embase) and free-text terms (Title/Abstract fields). Specifically, the boolean operators AND and OR were used to combine terms related to Spinal Cord Injury (e.g., Traumatic SCI, Spinal Injury), Buyang Huanwu Decoction (e.g., Buyang Huanwu Tang), and Randomized Controlled Trial. Taking PubMed as an example:
#1: ((Spinal Cord Injuries[Mesh] OR Spinal Cord Injury[Mesh] OR Spinal Cord Injuries, Traumatic[Mesh] OR Spinal Cord Injury, Traumatic[Mesh]) OR (Spinal Cord Injury OR SCI OR Spinal Injury))
#2: ((Buyang Huanwu Tang[Mesh] OR Buyang Huanwu Decoction[Mesh]) OR (Buyang Huanwu Tang OR Buyang Huanwu Decoction))
#3: (randomized controlled trial[pt] OR controlled clinical trial[pt] OR randomized[tiab] OR placebo[tiab] OR drug therapy[sh] OR randomly[tiab] OR trial[tiab] OR groups[tiab]) NOT (animals[mh] NOT humans[mh])
#4: #1 AND #2 AND #3
Filters and Limits: The Humans filter was activated where available. No language restrictions were imposed. Export Formats: All retrieved records were exported in standard citation formats (e.g., .nbib, .ris, or .ciw) and imported into reference management software (Table of Materials). Duplicates were removed using EndNote’s automatic deduplication function followed by manual verification.
Study Selection and Data Extraction Outcomes were screened based on strict inclusion criteria: (1) RCT study design; (2) participants diagnosed with SCI undergoing surgical treatment; (3) an experimental group receiving BYHWD plus routine surgical care versus a control group receiving routine surgical care alone; and (4) reporting of quantitative outcome measures including ASIA scores, evoked potentials (SEP/MEP), or clinical efficacy rates. Exclusion criteria encompassed animal studies, non-RCTs, duplicates, and studies with incomplete data. Two researchers independently screened titles, abstracts, and full texts. Disagreements regarding study eligibility were resolved operationally through immediate discussion; if consensus could not be reached, a third senior reviewer (Z.P.W.) made the final decision based on the full-text review. Data extraction was performed using a standardized spreadsheet form (see Table of Materials), capturing first author, year, demographics, interventions, and outcomes.
Risk of Bias Assessment The methodological quality of included RCTs was assessed independently by two reviewers using the Cochrane Risk of Bias Tool (RoB 2). For each included study, detailed justifications were provided for judgments across five domains: (1) Randomization process—sequence generation was considered low risk if computer-generated random numbers or randomization tables were used, and high risk if alternation, date of birth, or case record number was used; allocation concealment was judged low risk if centralized allocation, sealed opaque envelopes, or pharmacy-controlled randomization was employed, and high risk if open allocation schedule or unconcealed envelopes were used; (2) Deviations from intended interventions—blinding of participants and personnel was assessed as low risk for double-blind studies, some concerns for single-blind or objective outcomes, and high risk for open-label studies with subjective outcomes; (3) Missing outcome data—judged as low risk if <5% missing data with intention-to-treat analysis, moderate risk if 5%–20% missing, and high risk if >20% missing or per-protocol analysis with significant attrition (>15% difference between groups); (4) Measurement of the outcome—assessed as low risk if outcome assessors were blinded or outcomes were objective (e.g., mortality), and high risk if assessors were aware of group allocation and outcomes were subjective; (5) Selection of the reported result—evaluated by comparing protocols (if available) or methods sections against reported results, rated high risk if primary outcome was changed or selectively reported. Discrepancies between reviewers were resolved through discussion or consultation with a third reviewer (Sun).
Evidence quality was graded using the GRADE system across four levels: High quality (further research is very unlikely to change our confidence in the effect estimate; started as high for RCTs and rated down for limitations); Moderate quality (further research is likely to have an important impact on confidence and may change the estimate); Low quality (further research is very likely to have an important impact and is likely to change the estimate); and Very low quality (any estimate of effect is very uncertain). Quality was rated down from high for: risk of bias (serious limitations in randomization or blinding in >25% weight of studies), inconsistency (I2 > 50% or non-overlapping confidence intervals), indirectness (differences in population, intervention, comparator, or outcome measures), imprecision (optimal information size not met [<300 events for dichotomous outcomes or <400 participants for continuous outcomes] or 95% CI includes both meaningful benefit and harm), and publication bias (evident from funnel plot asymmetry, Egger's test p < 0.10, or <10 included studies with apparent small-study effects).
Statistical Analysis and Software Workflow Meta-analysis was performed using meta-analysis software (Table of Materials). Continuous data (e.g., ASIA scores, SEP/MEP amplitude) were entered as mean and standard deviation (SD) to calculate Mean Differences (MD) with 95% Confidence Intervals (95% CI). For dichotomous outcomes (e.g., efficacy rates), data were entered as event counts (number of events/total sample size) to calculate Risk Ratios (RR) with 95% Confidence Intervals (CI) using the Mantel-Haenszel method; for continuous outcomes (e.g., ASIA scores), the Inverse Variance method was employed to calculate Mean Differences (MD) with 95% CI. All analyses were initially performed using a fixed-effect model in the meta-analysis software (Table of Materials).
Heterogeneity assessment and model selection: Statistical heterogeneity across studies was quantified using the I2 statistic and Cochran’s Q test. The pre-specified threshold for significant heterogeneity was set at I2 > 50% or P < 0.10. If heterogeneity exceeded these thresholds, indicating that true effect sizes likely varied between studies rather than arising from chance alone, the analysis model was switched to a random-effects model (Der Simonian-Laird method) to provide a more conservative pooled estimate that accounts for between-study variance.
Sensitivity analysis: To test the robustness of findings, particularly for outcomes exhibiting high heterogeneity, sensitivity analyses were conducted by sequentially omitting one study at a time (leave-one-out method) and recalculating the pooled effect estimate for the remaining studies. If the omission of a single study substantially altered the direction or statistical significance of the overall effect (e.g., causing the 95% CI to cross the null value of 1.0 for RR or 0 for MD), that study was considered influential, and the stability of the results was interpreted with caution.
Publication bias assessment: Funnel plots were generated to visually assess publication bias only when at least 10 studies were included in the meta-analysis, as fewer studies result in insufficient power to distinguish true asymmetry from random scatter. When applicable, funnel plot asymmetry was interpreted considering the involvement of small-study effects; for analyses with fewer than 10 studies, publication bias assessment was not performed due to inadequate statistical power.