OUTCOME: Blood transfusion ANALYSIS: primary_all_designs STUDIES: 8 TREATMENTS: Conventional, Retzius, Ultra Original data: treat1 treat2 TE seTE Beyatli_2025 Retzius Ultra 1.1621 1.6395 Eden_2017 Conventional Retzius -0.0000 2.0123 Feng_2025 Conventional Retzius 0.0548 1.4239 Maddox_2013 Conventional Retzius -0.1484 2.0025 Nanchang_Deng_2021a Conventional Retzius 1.7237 1.4848 Qian_2024 Conventional Retzius -0.0000 0.8229 Ratanapornsompong_2020 Conventional Ultra -0.0000 0.8445 Sayyid_2017 Conventional Retzius 1.1086 1.6391 Number of treatment arms (by study): narms Beyatli_2025 2 Eden_2017 2 Feng_2025 2 Maddox_2013 2 Nanchang_Deng_2021a 2 Qian_2024 2 Ratanapornsompong_2020 2 Sayyid_2017 2 Results (random effects model): treat1 treat2 OR 95%-CI Beyatli_2025 Retzius Ultra 1.0597 [0.1968; 5.7056] Eden_2017 Conventional Retzius 1.2650 [0.4493; 3.5609] Feng_2025 Conventional Retzius 1.2650 [0.4493; 3.5609] Maddox_2013 Conventional Retzius 1.2650 [0.4493; 3.5609] Nanchang_Deng_2021a Conventional Retzius 1.2650 [0.4493; 3.5609] Qian_2024 Conventional Retzius 1.2650 [0.4493; 3.5609] Ratanapornsompong_2020 Conventional Ultra 1.3404 [0.3029; 5.9319] Sayyid_2017 Conventional Retzius 1.2650 [0.4493; 3.5609] Number of studies: k = 8 Number of pairwise comparisons: m = 8 Number of observations: o = 1700 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 0.7905 [0.2808; 2.2254] -0.45 0.6563 Ultra 0.7460 [0.1686; 3.3015] -0.39 0.6994 Quantifying heterogeneity / inconsistency: tau^2 < 0.0001; tau < 0.0001; I^2 = 0% [0.0%; 70.8%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 2.01 6 0.9187 Within designs 1.39 5 0.9258 Between designs 0.63 1 0.4291 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 0.5886 Retzius 0.5725 Conventional 0.3389 --- SUCRA RANKING --- SUCRA Ultra 0.5888 Retzius 0.5714 Conventional 0.3399 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 2.01 6 0.9187 Within designs 1.39 5 0.9258 Between designs 0.63 1 0.4291 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Retzius 1.39 5 0.9258 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.4291) 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 0.63 1 0.4291 0 0 --- 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 6 0.92 0.7905 0.6978 3.1967 0.2183 -0.79 0.4291 Ultra:Conventional 1 0.81 0.7460 1.0000 0.2183 4.5811 0.79 0.4291 Retzius:Ultra 1 0.27 1.0597 3.1967 0.6978 4.5811 0.79 0.4291 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)