The role of applied linguistics in enhancing medical translation accuracy: a corpus-based approach
Bibliographic record
Abstract
In the era of globalized healthcare, the precision of medical translation is essential for patient safety, professional communication, and international knowledge exchange. Inaccurate translations of medical documents may lead to clinical errors, ethical violations, or loss of trust between healthcare providers and patients. This article explores how principles and methods of applied linguistics, with a particular focus on corpus-based analysis, can improve the accuracy and consistency of English–Ukrainian medical translation. The study reviews current international and Ukrainian research on specialized translation, discussing how linguistic theory, cognitive processing, and sociocultural factors influence translation outcomes. It highlights corpus linguistics as an evidence-based approach that enables translators to identify authentic patterns of terminology, phraseology, and register in biomedical discourse. The paper also examines challenges in Ukrainian medical translation, including terminological inconsistency and insufficient corpus resources. The pedagogical implications for translator training within Ukrainian higher education are discussed, emphasizing data-driven learning and applied-linguistic competence. The integration of corpus methods and applied-linguistic frameworks offers a practical foundation for standardizing Ukrainian medical terminology and fostering the professionalization of translators. The article concludes by emphasizing future research prospects, such as developing large-scale English–Ukrainian medical corpora, incorporating natural language processing tools, and embedding corpus-informed pedagogy into translation curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".