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In Eurotransplant kidney allocation system (ETKAS), candidates can be considered unlimitedly for repeated re‐transplantation. Data on outcome and benefit are indeterminate. We performed a retrospective 15‐year patient and graft outcome data analysis from 1464 recipients of a third or fourth or higher sequential deceased donor renal transplantation (DDRT) from 42 transplant centers. Repeated re‐DDRT recipients were younger (mean 43.0 vs. 50.2 years) compared to first DDRT recipients. They received grafts with more favorable HLA matches (89.0% vs. 84.5%) but thereby no statistically significant improvement of patient and graft outcome was found as comparatively demonstrated in 1st DDRT. In the multivariate modeling accounting for confounding factors, mortality and graft loss after 3rd and ≥4th DDRT (P < 0.001 each) and death with functioning graft (DwFG) after 3rd DDRT (P = 0.001) were higher as compared to 1st DDRT. The incidence of primary nonfunction (PNF) was also significantly higher in re‐DDRT (12.7%) than in 1st DDRT (7.1%; P < 0.001). Facing organ shortage, increasing waiting time, and considerable mortality on dialysis, we question the current policy of repeated re‐DDRT. The data from this survey propose better HLA matching in first DDRT and second DDRT and careful selection of candidates, especially for ≥4th DDRT.
Digitization and transcription of historic documents offer new research opportunities for humanists and are the topics of many edition projects. However, manual work is still required for the main phases of layout recognition and the subsequent optical character recognition (OCR) of early printed documents. This paper describes and evaluates how deep learning approaches recognize text lines and can be extended to layout recognition using background knowledge. The evaluation was performed on five corpora of early prints from the 15th and 16th Centuries, representing a variety of layout features. While the main text with standard layouts could be recognized in the correct reading order with a precision and recall of up to 99.9%, also complex layouts were recognized at a rate as high as 90% by using background knowledge, the full potential of which was revealed if many pages of the same source were transcribed.