Statistical analysis of Anatomy final examination grades of Hungarian, German and English speaking medical students at the Semmelweis University
Bibliographic record
Abstract
Semmelweis University teaches an identical medical curriculum to students in three different languages; Hungarian, English or German. In the Hungarian group, both the students and the lecturers are native speakers, while in the German group, the students are native speakers and the lecturers use German as a foreign language. In the English speaking international group, both the students and lecturers use English as foreign language. We have recorded the results of 3550 Anatomy final examinations held between 2002 and 2011 to determine if there are differences in performance among the groups as measured by the number of students who have passed the practical (dissection) part of the exam followed by the theory exam. Statistical analysis has shown evidence of a relationship between a student's language and their performance on the practical exam. A greater proportion of German students (84.3%) passed the practical exam and then took the theory exam compared to the English (73.8%) or Hungarian students (72.0%) (p < 0.0001, Chi‐square = 46.805, df = 2, Cramer's V = 0.118 ). However, there were no differences among the languages in relation to the grades the students achieved on the theory part of the examination (p = 0.389, Kruskal‐Wallis H = 1.886, df = 2), thus suggesting that the level of tuition was the same for all three groups.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".