OUTCOME: Estimated blood loss ANALYSIS: primary_all_designs STUDIES: 20 TREATMENTS: Conventional, Retzius, Ultra Original data: treat1 treat2 TE seTE Beyatli_2025 Retzius Ultra 14.3000 4.6735 Chang_2018 Conventional Retzius 118.8100 53.9791 Eden_2017 Conventional Retzius -60.3290 56.0977 Elliott_2023 Conventional Retzius -34.0000 26.4274 Feng_2025 Conventional Retzius -8.8900 16.9679 Karsiyakali_2022 Conventional Retzius -6.0470 6.0961 Kwon_2014 Conventional Ultra -22.9000 22.4231 Lambert_2023 Conventional Retzius 104.6090 24.0823 Lin_2025 Conventional Ultra -19.7500 45.7227 Nagoya_RS_anatomy_OAB Conventional Retzius -226.4410 53.1788 Nanchang_Deng_2021a Conventional Retzius -6.9000 10.2103 Oshima_2023 Conventional Retzius -1.0000 9.8650 Pirzada_2025 Conventional Retzius -235.1500 43.0933 Qian_2024 Conventional Retzius -38.6110 11.1878 Qiu_2020 Conventional Retzius 17.6770 27.6393 Sayyid_2017 Conventional Retzius -17.5970 13.5606 Shimura_2025 Conventional Retzius 63.0000 50.1492 Wang_2021 Conventional Retzius -3.0000 12.5888 Zeng_2026 Conventional Retzius -0.0000 27.5458 Number of treatment arms (by study): narms Beyatli_2025 2 Chang_2018 2 Eden_2017 2 Elliott_2023 2 Feng_2025 2 Karsiyakali_2022 2 Kwon_2014 2 Lambert_2023 2 Lin_2025 2 Nagoya_RS_anatomy_OAB 2 Nanchang_Deng_2021a 2 Oshima_2023 2 Pirzada_2025 2 Qian_2024 2 Qiu_2020 2 Sayyid_2017 2 Shimura_2025 2 Wang_2021 2 Zeng_2026 2 Results (random effects model): treat1 treat2 MD 95%-CI Beyatli_2025 Retzius Ultra 3.3265 [-82.0959; 88.7490] Chang_2018 Conventional Retzius -17.9876 [-53.4009; 17.4257] Eden_2017 Conventional Retzius -17.9876 [-53.4009; 17.4257] Elliott_2023 Conventional Retzius -17.9876 [-53.4009; 17.4257] Feng_2025 Conventional Retzius -17.9876 [-53.4009; 17.4257] Karsiyakali_2022 Conventional Retzius -17.9876 [-53.4009; 17.4257] Kwon_2014 Conventional Ultra -14.6611 [-98.3816; 69.0595] Lambert_2023 Conventional Retzius -17.9876 [-53.4009; 17.4257] Lin_2025 Conventional Ultra -14.6611 [-98.3816; 69.0595] Nagoya_RS_anatomy_OAB Conventional Retzius -17.9876 [-53.4009; 17.4257] Nanchang_Deng_2021a Conventional Retzius -17.9876 [-53.4009; 17.4257] Oshima_2023 Conventional Retzius -17.9876 [-53.4009; 17.4257] Pirzada_2025 Conventional Retzius -17.9876 [-53.4009; 17.4257] Qian_2024 Conventional Retzius -17.9876 [-53.4009; 17.4257] Qiu_2020 Conventional Retzius -17.9876 [-53.4009; 17.4257] Sayyid_2017 Conventional Retzius -17.9876 [-53.4009; 17.4257] Shimura_2025 Conventional Retzius -17.9876 [-53.4009; 17.4257] Wang_2021 Conventional Retzius -17.9876 [-53.4009; 17.4257] Zeng_2026 Conventional Retzius -17.9876 [-53.4009; 17.4257] Number of studies: k = 19 Number of pairwise comparisons: m = 19 Number of observations: o = 3023 Number of treatments: n = 3 Number of designs: d = 3 Random effects model Treatment estimate (sm = 'MD', comparison: other treatments vs 'Conventional'): MD 95%-CI z p-value Conventional . . . . Retzius 17.9876 [-17.4257; 53.4009] 1.00 0.3195 Ultra 14.6611 [-69.0595; 98.3816] 0.34 0.7314 Quantifying heterogeneity / inconsistency: tau^2 = 4588.7255; tau = 67.7401; I^2 = 80.5% [70.1%; 87.3%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 87.26 17 < 0.0001 Within designs 85.53 16 < 0.0001 Between designs 1.73 1 0.1882 Details of network meta-analysis methods: - Frequentist graph-theoretical approach - Restricted maximum-likelihood estimator for tau^2 - Calculation of I^2 based on Q --- P-SCORE RANKING --- P-score Conventional 0.7373 Ultra 0.4481 Retzius 0.3147 --- SUCRA RANKING --- SUCRA Conventional 0.7370 Ultra 0.4496 Retzius 0.3134 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 87.26 17 < 0.0001 Within designs 85.53 16 < 0.0001 Between designs 1.73 1 0.1882 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Retzius 85.52 15 < 0.0001 Conventional:Ultra 0.00 1 0.9507 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.1882) Detached design Q df p-value Conventional:Retzius 0.00 0 -- Conventional:Ultra 0.00 0 -- Retzius:Ultra 0.00 0 -- Q statistic to assess consistency under the assumption of a full design-by-treatment interaction random effects model Q df p-value tau.within tau2.within Between designs 0.24 1 0.6208 32.7947 1075.4899 --- LOCAL INCONSISTENCY: NODE SPLITTING/SIDE --- Separate indirect from direct evidence (SIDE) using back-calculation method Random effects model: comparison k prop nma direct indir. Diff z p-value Retzius:Conventional 16 0.96 17.9876 17.1752 35.8375 -18.6622 -0.21 0.8331 Ultra:Conventional 2 0.63 14.6611 21.5375 2.8752 18.6622 0.21 0.8331 Retzius:Ultra 1 0.41 3.3265 14.3000 -4.3622 18.6622 0.21 0.8331 Legend: comparison - Treatment comparison k - Number of studies providing direct evidence prop - Direct evidence proportion nma - Estimated treatment effect (MD) in network meta-analysis direct - Estimated treatment effect (MD) derived from direct evidence indir. - Estimated treatment effect (MD) derived from indirect evidence Diff - Difference between direct and indirect treatment estimates z - z-value of test for disagreement (direct versus indirect) p-value - p-value of test for disagreement (direct versus indirect)