Dosing errors represent an important source of medication-related risk in interventional clinical trials, potentially affecting both participant safety and the validity of study outcomes. Despite their importance, systematic methods for predicting dosing error risk from trial design information remain largely unexplored. To address this gap, we organized the Clinical Trial Dosing Error Benchmark 2026 (CT-DEB’26) shared task, hosted at the CL4Health workshop at LREC 2026. The task focuses on predicting the risk of dosing errors in interventional clinical trials using heterogeneous information extracted from ClinicalTrials.gov, including structured protocol metadata and long-form textual descriptions. The released benchmark dataset contains over 42,000 clinical trial records spanning multiple study phases and therapeutic areas, annotated with binary labels indicating a significant high rate of dosing errors. Participants were asked to develop ML models capable of estimating trial-level dosing error risk, evaluated primarily using the ROC-AUC metric under strong class imbalance. The shared task was conducted in two phases and attracted 15 submissions in the development stage and 4 submissions in the final evaluation phase. This paper provides an overview of the shared task, describing the dataset construction, evaluation protocol, and participating systems. In addition, we present a schema-aware CatBoost baseline that leverages structured trial metadata and simple textual statistics, achieving ROC-AUC scores of 0.8606 and 0.8624 on the Phase 1 and Phase 2 leaderboards, respectively. We further summarize the approaches proposed by participating teams, which explore both feature-engineering pipelines and transformer-based text representations. The results highlight the importance of structured trial design variables and hybrid modeling strategies combining tabular and textual information. Finally, we discuss limitations of the benchmark and outline future directions for applying natural language processing and ML to improve medication safety in clinical trial design.

Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale electronic health records (EHRs). We propose an efficient end-to-end model, the Joint-NER-RE-Fourier (JNRF), to jointly learn the tasks of named entity recognition and relation extraction for documents of variable length. The architecture uses positional encoding and unitary batch sizes to process variable length documents and uses a weight-shared Fourier network layer for low-complexity token mixing. Finally, we reach the theoretical computational complexity lower bound for relation extraction using a selective pooling strategy and distance-aware attention weights with trainable polynomial distance functions. We evaluated the JNRF architecture using the 2018 N2C2 ADE benchmark to jointly extract medication-related entities and relations in variable-length EHR summaries. JNRF outperforms rolling window BERT with selective pooling by 0.42%, while being twice as fast to train. Compared to state-of-the-art BiLSTM-CRF architectures on the N2C2 ADE benchmark, results show that the proposed approach trains 22 times faster and reduces GPU memory consumption by 1.75 folds, with a reasonable performance tradeoff of 90%, without the use of external tools, hand-crafted rules or post-processing. Given the significant carbon footprint of deep learning models and the current energy crises, these methods could support efficient and cleaner information extraction in EHRs and other types of large-scale document databases.