A Comparison between the Difficulty Level (Readability) of English Medical Texts and Their Persian Translations
Bibliographic record
Abstract
Using foreign written materials in Iran's healthcare industry is very common, but it seems that there is a significant difference between the difficulty level of original texts and their corresponding translations. This study compares the readability level of English medical texts and their corresponding Persian translations. In this study, 50 translated booklets and their corresponding texts in English were assessed – all these booklets are translated versions of BMA publications and kept in Iran's National Library. Comparisons of these texts were made using Gunning Fog Index and SMOG Readability Index Grade. Then, significant difference between the data obtained from English medical texts and their Persian translations were made. A significant difference was observed between the number of multi-syllables words and readability scores in English medical texts and their corresponding Persian texts, but no significant difference was observed between the number of words and sentences in these two groups. Therefore, it is necessary to omit needless words, use fewer complex (multi-syllabuses) words, and use shorter sentences.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".