Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users’ preferences and significantly support the users’ understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph.

Objective: To assess in a prospective trial the influence of the amount of tissue resected at transurethral resection of the prostate (TURP) for benign prostatic enlargement on the symptom improvement as assessed by symptom scores. Methods: Between December 1996 and August 1998 a total of 138 men (mean age 68.2, range 53–89) with symptomatic benign prostatic enlargement who underwent TURP participated in this prospective study. Patients were assessed preoperatively with the International Prostate Symptom Score (IPSS), the American Urological Association Bother Score (AUA–BS) and the Benign Prostatic Hyperplasia Impact Index (BPH–II) as well as urinary flow rate measurements (Qmax) and prostate volume (PV) and residual urine determination by ultrasound. The amount of tissue resected was weighed. Patients were followed with reevaluation of Qmax, residual urine and the symptom and bother scores at 3 and 6 months. Results: A close correlation between preoperative PV (mean 49.0 ml, SD 22.0, range 13–140) and the resected tissue weight (RTW, mean 24.7 g, SD 18.0, range 6–128) was seen (r = 0.75, p<0.001). Age was correlated with preoperative PV (r = 0.23, p<0.05). While significant mean improvements in Qmax, residual volume and IPSS, AUA–BS and BPH–II were found 3 and 6 months postoperatively, a negative correlation was seen between the RTW and the IPSS, the AUA–BS and the BPH–II 3 months after TURP (r = –0.23, p<0.024; r = –0.23, p<0.025; r = –0.20, p = 0.05). No statistically significant correlation was seen between symptom change and the percentage of PV removed or the residual prostatic weight. Classification of the patients into groups depending on preoperative PV (<30, 31–50, 51–70 and >70 ml) showed a tendency for patients with larger PV to gain more symptom improvement postoperatively. Conclusions: Early symptom improvement after TURP will depend on the amount of tissue removed but the relationship is weak and affected by several other confounding factors. Apparently, the symptomatic improvement after TURP is not primarily dependent on the relative completeness of the resection. Patients with larger prostates and larger RTW tend to gain more symptomatic benefit from TURP than do patients with smaller prostates. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

No description available.

No description available.

No description available.

Background Relationships between in-hospital mortality and case volume were investigated for various patient groups in many empirical studies with mixed results. Typically, those studies relied on (semi-)parametric statistical models like logistic regression. Those models impose strong assumptions on the functional form of the relationship between outcome and case volume. The aim of this study was to determine associations between in-hospital mortality and hospital case volume using random forest as a flexible, nonparametric machine learning method. Methods We analyzed a sample of 753,895 hospital cases with stroke, myocardial infarction, ventilation > 24 h, COPD, pneumonia, and colorectal cancer undergoing colorectal resection treated in 233 German hospitals over the period 2016–2018. We derived partial dependence functions from random forest estimates capturing the relationship between the patient-specific probability of in-hospital death and hospital case volume for each of the six considered patient groups. Results Across all patient groups, the smallest hospital volumes were consistently related to the highest predicted probabilities of in-hospital death. We found strong relationships between in-hospital mortality and hospital case volume for hospitals treating a (very) small number of cases. Slightly higher case volumes were associated with substantially lower mortality. The estimated relationships between in-hospital mortality and case volume were nonlinear and nonmonotonic. Conclusion Our analysis revealed strong relationships between in-hospital mortality and hospital case volume in hospitals treating a small number of cases. The nonlinearity and nonmonotonicity of the estimated relationships indicate that studies applying conventional statistical approaches like logistic regression should consider these relationships adequately.