Assessment of English Translations of Chinese Titles of Clinical Trials Published in Medical Journals
Bibliographic record
Abstract
Objective:To accelerate the establishment and exchange of Cochrane Chinese clinical trial register,by accessing the quality of English translations of chinese title of clinical trials published in medical journals.Design:Cross sectional study.Method:Proofreading and registering the Chinese and English titles of clinical trials published in five medical journals in Procite software.Results:Among 341 records of the English translations affiliated in the original journals,there were 101 RCTs and 240 CCTs.The proportions of four essential factors(containing intervention,disease,participant and outcome in the titles)and three essential factors(one absence from the four factors)were 34 3% and 46 3%,respectively.The final outcomes of proofreading include:mistakes in translating 2 3%(8/341),in grammar 2 6%(9/341)and in spelling 5 0%(17/341).The proportion of English translations not conforming to the original Chinese titles added up to 27 0%(92/341),25 of which had improvement comparing with the original Chinese titles.The format of the English titles was not in accordance with the Vancouver standard style.There were another 341 records handsearched and translated by clinicians,including 261 RCTs and 80 CCTs.The titles containing four essential factors were 148(43 4%),while the three essential factors were 124(36 4%).Proofreading showed:mistakes in translating 7 9%(21/341),in grammar 13 5%(46/341),in spelling 10 0%(34/341)and in format 7 3%(25/341).There were 18 4%(63/341)which didn't conform to the original Chinese titles,among which,7 were improved comparing with the original Chinese titles.Conclusion:It remains to be improved in thenot conform to original titlewhen doing English translations in the original journals.The English translations by clinicians should also be improved and attention should be paid in the mistakes in translating,grammar and spelling.Careful checking by the journal editors,translators and proofreaders can improve the quality improvement of the English translations.
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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 | 0.376 | 0.796 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".