Medical practitioners' knowledge of Latin
Bibliographic record
Abstract
Latin terminology and abbreviations are used throughout medicine, particularly in patient notes and prescriptions. Such legal documents are an important form of communication between professionals, requiring detail and accuracy. However, doctors' understanding of Latin is poor: a community survey found pharmacists to be considerably better informed than general practitioners, particularly trainees.1 To determine the current knowledge of basic practical Latin, we conducted a survey of medical students and hospital doctors of all grades. Participants were required to give both the Latin words and English translations corresponding to 10 abbreviations in common use (Table 1). Of 120 questionnaires, 77 (64%) were returned; the response rate was particularly poor from consultants. The number of correct answers was disappointing, with only one doctor – a senior house officer – scoring more than two out of 10. No house officers (19) or medical students (33) gave any correct answers. Our results suggest an alarming situation in which doctors, whilst having insight into meaning, are not sure of exactly what they are writing on a daily basis. There are no indications that this might improve in the near future. The medical literature contains numerous examples in which misinterpretation of Latin abbreviations has resulted in prescribing errors.2 This raises serious medico-legal concerns – drug errors are the most common cause of medical mistakes, accounting for a quarter of successful negligence claims.3 The teaching of Latin in schools has declined by 75% over the past 30 years, with less than 10 000 pupils taking the subject at GCSE level in 2001.4 At the same time, access to medical education is being broadened to reflect the social, cultural and ethnic diversities of our population, encouraging those without a classical education to take up the challenge of a medical career.5 Whilst this is to be applauded, it has resulted in a cohort of professionals who continue to use abbreviations that they cannot translate. Herein lies a simple choice: either abandon such terminology in favour of English, or provide a limited education in medically relevant Latin at undergraduate level.1,2 As Latin is not only the basis of most European languages but also has widespread use across the world, we suggest that educating the next generation of doctors to understand what they are writing should be encouraged.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".