OUTCOME: Urinary continence at 3 months ANALYSIS: primary_all_designs STUDIES: 22 TREATMENTS: Conventional, Retzius, Ultra Original data: treat1 treat2 TE seTE Asimakopoulos_2019 Conventional Retzius -1.7636 0.6187 Beyatli_2025 Retzius Ultra 0.5553 0.4284 Dalela_Detroit_RCT_2015_2016 Conventional Retzius -1.0726 0.7036 Eden_2017 Conventional Retzius -2.7081 0.6200 Feng_2025 Conventional Retzius -1.2801 0.3958 Ficarra_2022 Conventional Retzius -1.5545 0.5748 Golubtsova_2021 Conventional Retzius -0.1603 1.0395 Kwon_2014 Conventional Ultra -1.3973 0.3842 Lambert_2023 Conventional Retzius -2.2012 0.4201 Lin_2025 Conventional Ultra -0.4796 1.6773 Maruyama_2020 Conventional Retzius -0.7587 0.5718 Nagoya_RS_anatomy_OAB Conventional Retzius -2.8478 0.8427 Nanchang_Deng_2021a Conventional Retzius -2.1267 0.4971 Qian_2024 Conventional Retzius -0.2298 0.2046 Ratanapornsompong_2020 Conventional Ultra -1.1059 0.4414 Sayyid_2017 Conventional Retzius -1.2593 0.2998 Siltari_2021 Conventional Ultra -0.2000 0.3195 Tan_2024 Conventional Ultra -0.3940 0.5468 Wang_2021 Conventional Retzius -1.9588 1.0832 Yee_2021 Conventional Retzius -2.6391 0.7540 Yilmaz_2023 Conventional Retzius -1.5041 0.5101 Zeng_2026 Conventional Retzius -2.4842 1.4887 Number of treatment arms (by study): narms Asimakopoulos_2019 2 Beyatli_2025 2 Dalela_Detroit_RCT_2015_2016 2 Eden_2017 2 Feng_2025 2 Ficarra_2022 2 Golubtsova_2021 2 Kwon_2014 2 Lambert_2023 2 Lin_2025 2 Maruyama_2020 2 Nagoya_RS_anatomy_OAB 2 Nanchang_Deng_2021a 2 Qian_2024 2 Ratanapornsompong_2020 2 Sayyid_2017 2 Siltari_2021 2 Tan_2024 2 Wang_2021 2 Yee_2021 2 Yilmaz_2023 2 Zeng_2026 2 Results (random effects model): treat1 treat2 OR 95%-CI Asimakopoulos_2019 Conventional Retzius 0.2190 [0.1488; 0.3224] Beyatli_2025 Retzius Ultra 2.0483 [1.0367; 4.0472] Dalela_Detroit_RCT_2015_2016 Conventional Retzius 0.2190 [0.1488; 0.3224] Eden_2017 Conventional Retzius 0.2190 [0.1488; 0.3224] Feng_2025 Conventional Retzius 0.2190 [0.1488; 0.3224] Ficarra_2022 Conventional Retzius 0.2190 [0.1488; 0.3224] Golubtsova_2021 Conventional Retzius 0.2190 [0.1488; 0.3224] Kwon_2014 Conventional Ultra 0.4486 [0.2439; 0.8253] Lambert_2023 Conventional Retzius 0.2190 [0.1488; 0.3224] Lin_2025 Conventional Ultra 0.4486 [0.2439; 0.8253] Maruyama_2020 Conventional Retzius 0.2190 [0.1488; 0.3224] Nagoya_RS_anatomy_OAB Conventional Retzius 0.2190 [0.1488; 0.3224] Nanchang_Deng_2021a Conventional Retzius 0.2190 [0.1488; 0.3224] Qian_2024 Conventional Retzius 0.2190 [0.1488; 0.3224] Ratanapornsompong_2020 Conventional Ultra 0.4486 [0.2439; 0.8253] Sayyid_2017 Conventional Retzius 0.2190 [0.1488; 0.3224] Siltari_2021 Conventional Ultra 0.4486 [0.2439; 0.8253] Tan_2024 Conventional Ultra 0.4486 [0.2439; 0.8253] Wang_2021 Conventional Retzius 0.2190 [0.1488; 0.3224] Yee_2021 Conventional Retzius 0.2190 [0.1488; 0.3224] Yilmaz_2023 Conventional Retzius 0.2190 [0.1488; 0.3224] Zeng_2026 Conventional Retzius 0.2190 [0.1488; 0.3224] Number of studies: k = 22 Number of pairwise comparisons: m = 22 Number of observations: o = 2968 Number of treatments: n = 3 Number of designs: d = 3 Random effects model Treatment estimate (sm = 'OR', comparison: other treatments vs 'Conventional'): OR 95%-CI z p-value Conventional . . . . Retzius 4.5660 [3.1020; 6.7209] 7.70 < 0.0001 Ultra 2.2291 [1.2117; 4.1006] 2.58 0.0100 Quantifying heterogeneity / inconsistency: tau^2 = 0.3141; tau = 0.5604; I^2 = 64.6% [43.6%; 77.7%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 56.44 20 < 0.0001 Within designs 56.39 19 < 0.0001 Between designs 0.05 1 0.8266 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 Retzius 0.9902 Ultra 0.5073 Conventional 0.0025 --- SUCRA RANKING --- SUCRA Retzius 0.9907 Ultra 0.5070 Conventional 0.0022 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 56.44 20 < 0.0001 Within designs 56.39 19 < 0.0001 Between designs 0.05 1 0.8266 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Retzius 49.50 15 < 0.0001 Conventional:Ultra 6.89 4 0.1417 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.8266) 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 0.07 1 0.7932 0.6812 0.4640 --- LOCAL INCONSISTENCY: NODE SPLITTING/SIDE --- Separate indirect from direct evidence (SIDE) using back-calculation method Random effects model: comparison k prop nma direct indir. RoR z p-value Retzius:Conventional 16 0.94 4.5660 4.6280 3.7382 1.2380 0.26 0.7922 Ultra:Conventional 5 0.82 2.2291 2.1453 2.6560 0.8077 -0.26 0.7922 Retzius:Ultra 1 0.24 2.0483 1.7425 2.1572 0.8077 -0.26 0.7922 Legend: comparison - Treatment comparison k - Number of studies providing direct evidence prop - Direct evidence proportion nma - Estimated treatment effect (OR) in network meta-analysis direct - Estimated treatment effect (OR) derived from direct evidence indir. - Estimated treatment effect (OR) derived from indirect evidence RoR - Ratio of Ratios (direct versus indirect) z - z-value of test for disagreement (direct versus indirect) p-value - p-value of test for disagreement (direct versus indirect)