ArticleScientific data2026
Transformer-based relation extraction and concept normalization using an annotated clinical trials corpus.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare professionals manually review electronic health records to select patients who meet eligibility criteria of clinical trials. Natural language processing offers a complement for this task, although few initiatives exist in Spanish. We present version 3 of the CT-EBM-SP corpus of 1200 clinical trials (292173 tokens), annotated with 23 entity types and 18 relation types, covering Unified Medical Language System (UMLS) semantic groups, drug-related information, temporal data, and negation/speculation. We encoded 11 attributes (e.g., event temporality and experiencer status) and normalized entities to UMLS Concept Unique Identifiers. The corpus contains 87037 entities, including nested and discontinuous entities, 16597 attributes and 68206 relationships. Inter-annotator agreement (IAA) achieved average F1 values of 0.861 (entities), 0.810 (attributes), and 0.791 (relations). 81.75% of entities were normalized (IAA: F1 = 0.966). We benchmarked this dataset by fine-tuning Transformer models for relation extraction (RE) and medical concept normalization (MCN). In RE, the average F1 ranged from 0.858 to 0.879, and for MCN, the accuracy at rank 1 was 0.896. The corpus and models are publicly available.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.