How clinical features are presented matters to weaker diagnosticians
Bibliographic record
Abstract
OBJECTIVES: This study aimed to test the extent to which the use of medicalese (i.e. formal medical terminology and semantic qualifiers) alters the test performance of medical graduates; to tease apart the extent to which any observed differences are driven by language difficulties versus differences in medical knowledge; and to assess the impact of varying the language used to present clinical features on the ability of the test to consistently discriminate between candidates. METHODS: Six clinical cases were manipulated in the context of pilot items on the Canadian national qualifying examination. Features indicative of two diagnoses were presented uniformly in lay terms, medical terminology and semantic qualifiers, respectively, and in mixed combinations (e.g. features of one diagnosis were presented using lay terminology and features of the other using medicalese). The rate at which the indicated diagnoses were named was considered as a function of language used, site of training, birthplace and medical knowledge (as measured by overall performance on the examination). RESULTS: In the mixed conditions, Canadian medical graduates were not influenced by the language used to present the cases, whereas international medical graduates (IMGs) were more likely to favour the diagnosis associated with medical terminology relative to that associated with lay terms. This was true regardless of whether the entire sample or only North American-born candidates were considered. Within the IMG cohort, high performers were not influenced by the language manipulation, whereas low performers were. Uniform use of lay terminology resulted in the highest test reliability compared with the other experimental conditions. CONCLUSIONS: The results indicate that the influence of medical terminology is driven more by substandard medical knowledge than by the language issues that challenge some candidates. Implications for both the assessment and education of medical professionals are discussed.
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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 | 0.008 | 0.081 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".