Evaluating the Efficacy of Large Language Models for Systematic Review and Meta-Analysis Screening
Bibliographic record
Abstract
ABSTRACT Background Systematic reviews and meta-analyses are essential for informed research and policymaking, yet they are typically resource-intensive and time-consuming. Recent advances in artificial intelligence and machine learning offer promising opportunities to streamline these processes. Objective To enhance the efficiency of systematic reviews, we explored the automation of various stages using GPT-3.5 Turbo. We assessed the model’s efficacy and performance by comparing it against three expert-conducted reviews across a comprehensive dataset of 24,534 studies. Methods The model’s performance was evaluated through a comparison with three expert reviews, utilizing a pseudo-K-folds permutation and a one-tailed ANOVA with an alpha level of 0.05 to ensure statistical validity. Key performance metrics such as accuracy, sensitivity, specificity, predictive values, F1-score, and the Matthews correlation coefficient were analyzed using two sets of prompts. Results Our approach significantly streamlined the systematic review process, which typically takes a year, reducing it to a few hours without sacrificing quality. In the initial screening phase, accuracy, specificity, and negative predictive values ranged between 80% and 95%. Sensitivity improved markedly during the second screening phase, demonstrating the model’s robustness when provided with more extensive data. Conclusion While ongoing refinements are needed, this tool represents a significant advancement in research methodologies, potentially making systematic reviews more accessible to a wider range of researchers. Impact Statement Our manuscript presents a novel review screening protocol built using open-source frameworks, which significantly enhances the systematic review process in terms of efficiency and cost-effectiveness. Leveraging the capabilities of GPT and embedding models, our protocol demonstrates the potential to transform a traditionally time-consuming and expensive task into an accelerated and economical operation, all while maintaining high standards of accuracy and reliability. Key Points GPT screening can streamline systematic reviews from a year-long, expensive process to just hours at minimal cost. Validated across different topics, the protocol exhibits high reliability and consistency in study inclusion. The AI-driven process reduces human bias, with prompt optimization considerably improving sensitivity.
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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 | 0.511 | 0.822 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".