OUTCOME: Operative time ANALYSIS: primary_all_designs STUDIES: 22 TREATMENTS: Conventional, Retzius, Ultra Original data: treat1 treat2 TE seTE Asimakopoulos_2019 Conventional Retzius -16.0000 8.3540 Beyatli_2025 Retzius Ultra 13.9000 2.5166 Chang_2018 Conventional Retzius 2.1700 11.8713 Eden_2017 Conventional Retzius 36.1300 21.5317 Elliott_2023 Conventional Retzius -33.0000 9.1902 Feng_2025 Conventional Retzius -2.4300 2.2399 Karsiyakali_2022 Conventional Retzius 38.2410 10.9667 Kwon_2014 Conventional Ultra 1.9000 7.6815 Lambert_2023 Conventional Retzius -12.5000 4.5909 Nagoya_RS_anatomy_OAB Conventional Retzius 7.0000 7.7663 Nanchang_Deng_2021a Conventional Retzius -10.7000 4.1680 Oshima_2023 Conventional Retzius 9.8000 5.5739 Pirzada_2025 Conventional Retzius -85.8000 7.3747 Qian_2024 Conventional Retzius -8.2450 2.8576 Qiu_2020 Conventional Retzius 28.2320 6.1803 Ratanapornsompong_2020 Conventional Ultra 6.8700 8.9763 Siltari_2021 Conventional Ultra 0.3000 2.5825 Tahra_2021 Conventional Retzius -10.0000 20.8322 Wang_2021 Conventional Retzius -27.0000 5.2032 Yee_2021 Conventional Retzius 14.0000 14.2141 Yilmaz_2023 Conventional Retzius 21.6700 42.7548 Zeng_2026 Conventional Retzius -11.8100 9.0430 Number of treatment arms (by study): narms Asimakopoulos_2019 2 Beyatli_2025 2 Chang_2018 2 Eden_2017 2 Elliott_2023 2 Feng_2025 2 Karsiyakali_2022 2 Kwon_2014 2 Lambert_2023 2 Nagoya_RS_anatomy_OAB 2 Nanchang_Deng_2021a 2 Oshima_2023 2 Pirzada_2025 2 Qian_2024 2 Qiu_2020 2 Ratanapornsompong_2020 2 Siltari_2021 2 Tahra_2021 2 Wang_2021 2 Yee_2021 2 Yilmaz_2023 2 Zeng_2026 2 Results (random effects model): treat1 treat2 MD 95%-CI Asimakopoulos_2019 Conventional Retzius -5.8999 [-17.9989; 6.1991] Beyatli_2025 Retzius Ultra 10.1432 [-16.1490; 36.4354] Chang_2018 Conventional Retzius -5.8999 [-17.9989; 6.1991] Eden_2017 Conventional Retzius -5.8999 [-17.9989; 6.1991] Elliott_2023 Conventional Retzius -5.8999 [-17.9989; 6.1991] Feng_2025 Conventional Retzius -5.8999 [-17.9989; 6.1991] Karsiyakali_2022 Conventional Retzius -5.8999 [-17.9989; 6.1991] Kwon_2014 Conventional Ultra 4.2433 [-20.6912; 29.1777] Lambert_2023 Conventional Retzius -5.8999 [-17.9989; 6.1991] Nagoya_RS_anatomy_OAB Conventional Retzius -5.8999 [-17.9989; 6.1991] Nanchang_Deng_2021a Conventional Retzius -5.8999 [-17.9989; 6.1991] Oshima_2023 Conventional Retzius -5.8999 [-17.9989; 6.1991] Pirzada_2025 Conventional Retzius -5.8999 [-17.9989; 6.1991] Qian_2024 Conventional Retzius -5.8999 [-17.9989; 6.1991] Qiu_2020 Conventional Retzius -5.8999 [-17.9989; 6.1991] Ratanapornsompong_2020 Conventional Ultra 4.2433 [-20.6912; 29.1777] Siltari_2021 Conventional Ultra 4.2433 [-20.6912; 29.1777] Tahra_2021 Conventional Retzius -5.8999 [-17.9989; 6.1991] Wang_2021 Conventional Retzius -5.8999 [-17.9989; 6.1991] Yee_2021 Conventional Retzius -5.8999 [-17.9989; 6.1991] Yilmaz_2023 Conventional Retzius -5.8999 [-17.9989; 6.1991] Zeng_2026 Conventional Retzius -5.8999 [-17.9989; 6.1991] Number of studies: k = 22 Number of pairwise comparisons: m = 22 Number of observations: o = 3246 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 5.8999 [ -6.1991; 17.9989] 0.96 0.3392 Ultra -4.2433 [-29.1777; 20.6912] -0.33 0.7387 Quantifying heterogeneity / inconsistency: tau^2 = 600.3894; tau = 24.5028; I^2 = 90.8% [87.3%; 93.3%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 217.17 20 < 0.0001 Within designs 214.59 19 < 0.0001 Between designs 2.58 1 0.1084 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 Ultra 0.7029 Conventional 0.5999 Retzius 0.1972 --- SUCRA RANKING --- SUCRA Ultra 0.7044 Conventional 0.5974 Retzius 0.1982 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 217.17 20 < 0.0001 Within designs 214.59 19 < 0.0001 Between designs 2.58 1 0.1084 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Retzius 214.08 17 < 0.0001 Conventional:Ultra 0.51 2 0.7737 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.1084) 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 0.04 1 0.8321 18.5542 344.2580 --- 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 18 0.95 5.8999 5.6525 10.9934 -5.3409 -0.18 0.8557 Ultra:Conventional 3 0.75 -4.2433 -2.9066 -8.2475 5.3409 0.18 0.8557 Retzius:Ultra 1 0.30 10.1432 13.9000 8.5591 5.3409 0.18 0.8557 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)