OUTCOME: Overall positive surgical margins ANALYSIS: primary_all_designs STUDIES: 29 TREATMENTS: Conventional, Retzius, Ultra Original data (with adjusted standard errors for multi-arm studies): treat1 treat2 TE seTE seTE.adj narms multiarm Beyatli_2025 Retzius Ultra 0.2401 0.4235 0.4652 2 Chang_2018 Conventional Retzius 0.1780 0.5973 0.6276 2 Dalela_Detroit_RCT_2015_2016 Conventional Retzius -0.7732 0.4828 0.5198 2 Eden_2017 Conventional Retzius -0.4321 0.9416 0.9611 2 Elliott_2023 Conventional Retzius -0.2471 0.4980 0.5339 2 Ficarra_2022 Conventional Retzius -0.1479 0.4821 0.5191 2 Karsiyakali_2022 Conventional Retzius 0.2557 0.3416 0.3921 2 Kowalczyk_Georgetown_series Conventional Ultra -0.4939 0.2673 0.3294 2 Kwon_2014 Conventional Ultra 0.6231 0.3564 0.4051 2 Lambert_2023 Conventional Retzius -0.6135 0.2989 0.3556 2 Lim_2014 Conventional Retzius -0.6123 0.5028 0.5384 2 Lin_2025 Conventional Ultra 1.1856 0.9094 0.9296 2 Maddox_2013 Conventional Retzius 0.0661 0.2657 0.3281 2 Nagoya_RS_anatomy_OAB Conventional Retzius -0.4418 0.6696 0.6968 2 Nanchang_Deng_2021a Conventional Retzius 0.3642 0.4216 0.4635 2 Pirzada_2025 Conventional Retzius -0.3330 0.2938 0.3513 2 Puliatti_2019 Conventional Ultra -1.2763 0.6200 0.6492 2 Qian_2024 Conventional Retzius -0.0790 0.2811 0.3407 2 Qiu_2020 Conventional Retzius -0.5980 0.4970 0.5330 2 Ratanapornsompong_2020 Conventional Ultra -0.0945 0.4349 0.4756 2 Shimura_2025 Conventional Retzius -0.6947 0.3756 0.4221 2 Siltari_2021 Conventional Ultra -0.2144 0.3445 0.3947 2 Tahra_2021 Conventional Retzius 0.1431 0.4436 0.4836 2 Tan_2024 Conventional Ultra 0.4055 0.6055 0.6354 2 Vargo_2024 Retzius Ultra 1.1053 0.4684 0.6424 3 * Vargo_2024 Conventional Ultra 0.2856 0.4382 0.5722 3 * Vargo_2024 Conventional Retzius -0.8197 0.4478 0.5916 3 * Wang_2021 Conventional Retzius -0.5390 0.7067 0.7324 2 Yee_2021 Conventional Retzius 1.0544 0.6079 0.6376 2 Yilmaz_2023 Conventional Retzius -0.1780 0.4224 0.4642 2 Zeng_2026 Conventional Retzius -0.2283 0.3907 0.4355 2 Number of treatment arms (by study): narms Beyatli_2025 2 Chang_2018 2 Dalela_Detroit_RCT_2015_2016 2 Eden_2017 2 Elliott_2023 2 Ficarra_2022 2 Karsiyakali_2022 2 Kowalczyk_Georgetown_series 2 Kwon_2014 2 Lambert_2023 2 Lim_2014 2 Lin_2025 2 Maddox_2013 2 Nagoya_RS_anatomy_OAB 2 Nanchang_Deng_2021a 2 Pirzada_2025 2 Puliatti_2019 2 Qian_2024 2 Qiu_2020 2 Ratanapornsompong_2020 2 Shimura_2025 2 Siltari_2021 2 Tahra_2021 2 Tan_2024 2 Vargo_2024 3 Wang_2021 2 Yee_2021 2 Yilmaz_2023 2 Zeng_2026 2 Results (random effects model): treat1 treat2 OR 95%-CI Beyatli_2025 Retzius Ultra 1.2533 [0.8900; 1.7649] Chang_2018 Conventional Retzius 0.7949 [0.6544; 0.9656] Dalela_Detroit_RCT_2015_2016 Conventional Retzius 0.7949 [0.6544; 0.9656] Eden_2017 Conventional Retzius 0.7949 [0.6544; 0.9656] Elliott_2023 Conventional Retzius 0.7949 [0.6544; 0.9656] Ficarra_2022 Conventional Retzius 0.7949 [0.6544; 0.9656] Karsiyakali_2022 Conventional Retzius 0.7949 [0.6544; 0.9656] Kowalczyk_Georgetown_series Conventional Ultra 0.9963 [0.7357; 1.3492] Kwon_2014 Conventional Ultra 0.9963 [0.7357; 1.3492] Lambert_2023 Conventional Retzius 0.7949 [0.6544; 0.9656] Lim_2014 Conventional Retzius 0.7949 [0.6544; 0.9656] Lin_2025 Conventional Ultra 0.9963 [0.7357; 1.3492] Maddox_2013 Conventional Retzius 0.7949 [0.6544; 0.9656] Nagoya_RS_anatomy_OAB Conventional Retzius 0.7949 [0.6544; 0.9656] Nanchang_Deng_2021a Conventional Retzius 0.7949 [0.6544; 0.9656] Pirzada_2025 Conventional Retzius 0.7949 [0.6544; 0.9656] Puliatti_2019 Conventional Ultra 0.9963 [0.7357; 1.3492] Qian_2024 Conventional Retzius 0.7949 [0.6544; 0.9656] Qiu_2020 Conventional Retzius 0.7949 [0.6544; 0.9656] Ratanapornsompong_2020 Conventional Ultra 0.9963 [0.7357; 1.3492] Shimura_2025 Conventional Retzius 0.7949 [0.6544; 0.9656] Siltari_2021 Conventional Ultra 0.9963 [0.7357; 1.3492] Tahra_2021 Conventional Retzius 0.7949 [0.6544; 0.9656] Tan_2024 Conventional Ultra 0.9963 [0.7357; 1.3492] Vargo_2024 Retzius Ultra 1.2533 [0.8900; 1.7649] Vargo_2024 Conventional Ultra 0.9963 [0.7357; 1.3492] Vargo_2024 Conventional Retzius 0.7949 [0.6544; 0.9656] Wang_2021 Conventional Retzius 0.7949 [0.6544; 0.9656] Yee_2021 Conventional Retzius 0.7949 [0.6544; 0.9656] Yilmaz_2023 Conventional Retzius 0.7949 [0.6544; 0.9656] Zeng_2026 Conventional Retzius 0.7949 [0.6544; 0.9656] Number of studies: k = 29 Number of pairwise comparisons: m = 31 Number of observations: o = 4396 Number of treatments: n = 3 Number of designs: d = 4 Random effects model Treatment estimate (sm = 'OR', comparison: other treatments vs 'Conventional'): OR 95%-CI z p-value Conventional . . . . Retzius 1.2580 [1.0356; 1.5281] 2.31 0.0208 Ultra 1.0037 [0.7412; 1.3593] 0.02 0.9808 Quantifying heterogeneity / inconsistency: tau^2 = 0.0371; tau = 0.1925; I^2 = 17.9% [0.0%; 48.1%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 34.12 28 0.1970 Within designs 29.69 25 0.2360 Between designs 4.43 3 0.2190 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 0.7496 Ultra 0.6962 Retzius 0.0542 --- SUCRA RANKING --- SUCRA Conventional 0.7468 Ultra 0.6992 Retzius 0.0540 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 34.12 28 0.1970 Within designs 29.69 25 0.2360 Between designs 4.43 3 0.2190 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Ultra 12.67 6 0.0485 Conventional:Retzius 17.02 19 0.5887 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.2190) Detached design Q df p-value Conventional:Retzius:Ultra 0.15 1 0.7031 Conventional:Retzius 2.05 2 0.3595 Conventional:Ultra 2.73 2 0.2560 Retzius:Ultra 4.41 2 0.1100 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 3.67 3 0.2996 0.1801 0.0324 --- 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 21 0.96 1.2580 1.2399 1.7149 0.7231 -0.67 0.5005 Ultra:Conventional 8 0.87 1.0037 1.0550 0.7099 1.4861 0.85 0.3961 Retzius:Ultra 2 0.26 1.2533 1.8891 1.0851 1.7409 1.39 0.1639 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)