TY - JOUR A1 - Huflage, Henner A1 - Fieber, Tabea A1 - Färber, Christian A1 - Knarr, Jonas A1 - Veldhoen, Simon A1 - Jordan, Martin C. A1 - Gilbert, Fabian A1 - Bley, Thorsten Alexander A1 - Meffert, Rainer H. A1 - Grunz, Jan-Peter A1 - Schmalzl, Jonas T1 - Interobserver reliability of scapula fracture classifications in intra- and extra-articular injury patterns JF - BMC Musculoskeletal Disorders N2 - Background Morphology and glenoid involvement determine the necessity of surgical management in scapula fractures. While being present in only a small share of patients with shoulder trauma, numerous classification systems have been in use over the years for categorization of scapula fractures. The purpose of this study was to evaluate the established AO/OTA classification in comparison to the classification system of Euler and Rüedi (ER) with regard to interobserver reliability and confidence in clinical practice. Methods Based on CT imaging, 149 patients with scapula fractures were retrospectively categorized by two trauma surgeons and two radiologists using the classification systems of ER and AO/OTA. To measure the interrater reliability, Fleiss kappa (κ) was calculated independently for both fracture classifications. Rater confidence was stated subjectively on a five-point scale and compared with Wilcoxon signed rank tests. Additionally, we computed the intraclass correlation coefficient (ICC) based on absolute agreement in a two-way random effects model to assess the diagnostic confidence agreement between observers. Results In scapula fractures involving the glenoid fossa, interrater reliability was substantial (κ = 0.722; 95% confidence interval [CI] 0.676–0.769) for the AO/OTA classification in contrast to moderate agreement (κ = 0.579; 95% CI 0.525–0.634) for the ER classification system. Diagnostic confidence for intra-articular fracture patterns was superior using the AO/OTA classification compared to ER (p < 0.001) with higher confidence agreement (ICC: 0.882 versus 0.831). For extra-articular fractures, ER (κ = 0.817; 95% CI 0.771–0.863) provided better interrater reliability compared to AO/OTA (κ = 0.734; 95% CI 0.692–0.776) with higher diagnostic confidence (p < 0.001) and superior agreement between confidence ratings (ICC: 0.881 versus 0.912). Conclusions The AO/OTA classification is most suitable to categorize intra-articular scapula fractures with glenoid involvement, whereas the classification system of Euler and Rüedi appears to be superior in extra-articular injury patterns with fractures involving only the scapula body, spine, acromion and coracoid process. KW - confidence KW - scapula KW - glenoid KW - fracture KW - classification KW - reliability Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299795 VL - 23 IS - 1 ER - TY - JOUR A1 - Waltmann, Maria A1 - Schlagenhauf, Florian A1 - Deserno, Lorenz T1 - Sufficient reliability of the behavioral and computational readouts of a probabilistic reversal learning task JF - Behavior Research Methods N2 - Task-based measures that capture neurocognitive processes can help bridge the gap between brain and behavior. To transfer tasks to clinical application, reliability is a crucial benchmark because it imposes an upper bound to potential correlations with other variables (e.g., symptom or brain data). However, the reliability of many task readouts is low. In this study, we scrutinized the retest reliability of a probabilistic reversal learning task (PRLT) that is frequently used to characterize cognitive flexibility in psychiatric populations. We analyzed data from N = 40 healthy subjects, who completed the PRLT twice. We focused on how individual metrics are derived, i.e., whether data were partially pooled across participants and whether priors were used to inform estimates. We compared the reliability of the resulting indices across sessions, as well as the internal consistency of a selection of indices. We found good to excellent reliability for behavioral indices as derived from mixed-effects models that included data from both sessions. The internal consistency was good to excellent. For indices derived from computational modeling, we found excellent reliability when using hierarchical estimation with empirical priors and including data from both sessions. Our results indicate that the PRLT is well equipped to measure individual differences in cognitive flexibility in reinforcement learning. However, this depends heavily on hierarchical modeling of the longitudinal data (whether sessions are modeled separately or jointly), on estimation methods, and on the combination of parameters included in computational models. We discuss implications for the applicability of PRLT indices in psychiatric research and as diagnostic tools. KW - probabilistic reversal learning KW - reliability KW - reinforcement learning KW - computational modeling KW - hierarchical modeling Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324246 SN - 1554-3528 VL - 54 IS - 6 ER -