OUTCOME: Clavien-Dindo grade I-II complications 
ANALYSIS: primary_all_designs 
STUDIES: 7 
TREATMENTS: Conventional, Retzius, Ultra 

Original data:

                       treat1  treat2      TE   seTE
Beyatli_2025          Retzius   Ultra  0.3051 0.4984
Eden_2017        Conventional Retzius  1.6601 1.5655
Karsiyakali_2022 Conventional Retzius  0.8581 1.1622
Lambert_2023     Conventional Retzius  0.8723 0.4140
Lim_2014         Conventional Retzius  0.9808 0.8620
Yee_2021         Conventional Retzius  0.4477 0.6740
Yilmaz_2023      Conventional Retzius -0.0000 0.6606

Number of treatment arms (by study):
                 narms
Beyatli_2025         2
Eden_2017            2
Karsiyakali_2022     2
Lambert_2023         2
Lim_2014             2
Yee_2021             2
Yilmaz_2023          2

Results (random effects model):

                       treat1  treat2     OR           95%-CI
Beyatli_2025          Retzius   Ultra 1.3567 [0.5108; 3.6037]
Eden_2017        Conventional Retzius 1.9722 [1.1409; 3.4093]
Karsiyakali_2022 Conventional Retzius 1.9722 [1.1409; 3.4093]
Lambert_2023     Conventional Retzius 1.9722 [1.1409; 3.4093]
Lim_2014         Conventional Retzius 1.9722 [1.1409; 3.4093]
Yee_2021         Conventional Retzius 1.9722 [1.1409; 3.4093]
Yilmaz_2023      Conventional Retzius 1.9722 [1.1409; 3.4093]

Number of studies: k = 7
Number of pairwise comparisons: m = 7
Number of observations: o = 975
Number of treatments: n = 3
Number of designs: d = 2

Random effects model

Treatment estimate (sm = 'OR', comparison: other treatments vs 'Conventional'):
                 OR           95%-CI     z p-value
Conventional      .                .     .       .
Retzius      0.5071 [0.2933; 0.8765] -2.43  0.0150
Ultra        0.3737 [0.1220; 1.1452] -1.72  0.0850

Quantifying heterogeneity / inconsistency:
tau^2 < 0.0001; tau < 0.0001; I^2 = 0% [0.0%; 74.6%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           1.93    5  0.8585
Within designs  1.93    5  0.8585
Between designs 0.00    0      --

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         0.8436
Retzius       0.6314
Conventional  0.0250

--- SUCRA RANKING ---
              SUCRA
Ultra        0.8434
Retzius      0.6328
Conventional 0.0238

- based on 20000 simulations

--- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT ---
Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           1.93  5  0.8585
Within designs  1.93  5  0.8585
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

               Design    Q df p-value
 Conventional:Retzius 1.93  5  0.8585

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 0.00  0      --          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 1.00 0.5071 0.5071      .   . .       .
   Ultra:Conventional 0    0 0.3737      . 0.3737   . .       .
        Retzius:Ultra 1 1.00 1.3567 1.3567      .   . .       .

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)