THE EFFECT OF ENGLISH-LANGUAGE RESTRICTION ON SYSTEMATIC REVIEW-BASED META-ANALYSES: A SYSTEMATIC REVIEW OF EMPIRICAL STUDIES
Bibliographic record
Abstract
OBJECTIVES: The English language is generally perceived to be the universal language of science. However, the exclusive reliance on English-language studies may not represent all of the evidence. Excluding languages other than English (LOE) may introduce a language bias and lead to erroneous conclusions. STUDY DESIGN AND SETTING: We conducted a comprehensive literature search using bibliographic databases and grey literature sources. Studies were eligible for inclusion if they measured the effect of excluding randomized controlled trials (RCTs) reported in LOE from systematic review-based meta-analyses (SR/MA) for one or more outcomes. RESULTS: None of the included studies found major differences between summary treatment effects in English-language restricted meta-analyses and LOE-inclusive meta-analyses. Findings differed about the methodological and reporting quality of trials reported in LOE. The precision of pooled estimates improved with the inclusion of LOE trials. CONCLUSIONS: Overall, we found no evidence of a systematic bias from the use of language restrictions in systematic review-based meta-analyses in conventional medicine. Further research is needed to determine the impact of language restriction on systematic reviews in particular fields of medicine.
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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 | 0.524 | 0.771 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| 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; 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".