ABSTRACT
BACKGROUND: Abdominal wall reconstruction (AWR) is a treatment option for structural defects of the abdominal wall. The most frequently cited publications related to AWR have not been quantitatively or qualitatively assessed. This bibliometric analysis characterizes and assesses the most frequently cited AWR publications, to identify trends, gaps, and guide future efforts for the international research community. METHODS: The 100 most cited publications in AWR were identified on Web of Science, across all available journal years (from May 1964 to December 2023). Study details, including the citation count, main content focus, and outcome measures, were extracted and tabulated from each publication. Oxford Centre for Evidence-Based Medicine levels of evidence (LOE) of each study were also assessed. RESULTS: The 100 most cited publications in AWR were cited by a total of 9674 publications. Citations per publication ranged from 43 to 414 (mean 96.7 ± 52.48). Most publications were LOE 3 (n = 60), representative of the large number of retrospective cohort studies. The number of publications for LOE 5, 4, 3, 2, and 1 was 21, 2, 60, 2, and 12, respectively. The main content focus was surgical technique in 44 publications followed by outcomes in 38 publications. Patient-reported outcome measures were used in 3 publications, and no publications reported validated esthetic outcome measures. CONCLUSIONS: Overall, 3 was the LOE for most frequently cited AWR publications, with more publications below LOE 3 than above LOE 3. Validated outcome measures and patient-reported outcome measures were infrequently incorporated in the studies evaluated.
ABSTRACT
While modelling and simulation are powerful techniques for exploring complex phenomena, if they are not coupled with suitable real-world data any results obtained are likely to require extensive validation. We consider this problem in the context of search game modelling, and suggest that both demographic and behaviour data are used to configure certain model parameters. We show this integration in practice by using a combined dataset of over 150,000 individuals to configure a specific search game model that captures the environment, population, interventions and individual behaviours relating to winter health service pressures. The presence of this data enables us to more accurately explore the potential impact of service pressure interventions, which we do across 33,000 simulations using a computational version of the model. We find government advice to be the best-performing intervention in simulation, in respect of improved health, reduced health inequalities, and thus reduced pressure on health service utilisation.
