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The CREATE-ECLA Randomized Controlled TrialThe CREATE-ECLA Trial Group Investigators* See also pp 427 and 489.

Background Treatment of undifferentiated embryonal sarcoma of the liver (UESL) is a great clinical challenge due to its rarity. This study aims to examine the long‐term survival of UESL patients after treatment using different therapeutic modalities. Methods A pooled analysis of individual data was performed on all UESL patients obtained from literature search (n = 307) and our institution (n = 1). Results The 5‐year overall survival rate of the 308 patients was 65.8%, 70% for partial hepatectomy group (n = 271), 78.9% for liver transplantation group (n = 14) and 6.6% for nonsurgical treatment group (n = 23). For patients receiving partial hepatectomy, paediatric patients, radical resection and combined chemotherapy were independent predictors for improved survival. Conclusion Radical hepatectomy combined chemotherapy should be considered as the preferred treatment option for USEL. Liver transplantation appears to be a reasonable alternative for unresectable disease.

By introducing a small set of additional parameters, a probe learns to solve specific linguistic tasks (e.g., dependency parsing) in a supervised manner using feature representations (e.g., contextualized embeddings). The effectiveness of such probing tasks is taken as evidence that the pre-trained model encodes linguistic knowledge. However, this approach of evaluating a language model is undermined by the uncertainty of the amount of knowledge that is learned by the probe itself. Complementary to those works, we propose a parameter-free probing technique for analyzing pre-trained language models (e.g., BERT). Our method does not require direct supervision from the probing tasks, nor do we introduce additional parameters to the probing process. Our experiments on BERT show that syntactic trees recovered from BERT using our method are significantly better than linguistically-uninformed baselines. We further feed the empirically induced dependency structures into a downstream sentiment classification task and find its improvement compatible with or even superior to a human-designed dependency schema.

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