Journal impact factor, trial effect size, and methodological quality appear scantly related: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: As systematic reviews' limited coverage of the medical literature necessitates decision-making based on unsystematic review, we investigated a possible advantage of systematic review (aside from dataset size and systematic analysis): does systematic review avoid potential bias in sampling primary studies from high impact factor journals? If randomized controlled trials (RCTs) reported in higher-impact journals present different treatment benefits than RCTs reported in lower-impact journals, readers who focus on higher-impact journals for their rapid literature reviews may introduce bias which could be mitigated by complete, systematic sampling. METHODS: We randomly sampled Cochrane Library (20 July 2005) treatment reviews that measured mortality as a binary outcome, published in English or French, with at least five RCTs with one or more deaths. Our domain-based assessment of risk of bias included funding source, randomness of allocation sequence, blinding, and allocation concealment. The primary analysis employed logistic regression by a generalized linear model with a generalized estimating equation to estimate the association between various factors and publication in a journal with a high journal impact factor (JIF). RESULTS: From the 29 included systematic reviews, 189 RCTs contributed data. However, in the primary analyses comparing RCT results within meta-analyses, there was no statistically significant association: unadjusted odds of greater than 50% mortality protection in high-JIF (> 5) journals were 1.4 (95% CI 0.42, 4.4) and adjusted, 2.5 (95% CI 0.6, 10). Elements of study quality were weakly, inconsistently, and not statistically significantly correlated with journal impact factor. CONCLUSIONS: Journal impact factor may have little to no association with study results, or methodological quality, but the evidence is very uncertain.
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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 | 0.357 | 0.701 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.051 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 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".