Leveraging open-source large language models (LLMs) in scoping reviews: a case study on disability and AI applications
Bibliographic record
Abstract
BACKGROUND: Large language models (LLMs) have the potential to offer solutions for automating many of the manual tasks involved in scientific reviews, including data extraction, literature screening, summarization, and quality assessment. OBJECTIVES: This study aims to evaluate the performance of LLMs in the task of title and abstract screening and full-text data extraction of a scoping review study, by identifying their effectiveness, efficiency, and potential integration into human-based and manual tasks. MATERIALS AND METHOD: The following key three steps of a scientific scoping review were automated: 1) Title and Abstract Screening, 2) Full-Text Screening, and 3) Data Extraction based on nine study dimensions. The four most recent lightweight open-source LLMs -Mistral, Vicuna, and Llama 3.2 with 1B and 3B parameters- were applied and evaluated through the steps. RESULTS: Llama 3.2-3B demonstrated the best performance in the title and abstract screening, achieving an accuracy of 66 %, excelling in the exclusion of papers. For full-text screening, it maintained the highest overall accuracy of 65 %, effectively identifying excluded papers. In data extraction, the Mistral model outperformed others across most dimensions, though Llama 3.2-3B excelled in extracting objectives and study implications. DISCUSSION AND CONCLUSION: The present study underscores both the potential and limitations of LLMs in automating scoping reviews. Automating the entire scoping review without human intervention is sub-optimal. Using a more controlled approach balances the strengths of LLMs with the need for human judgment, supporting not only the replication of scientific reviews but also their continuous refinement and follow-up over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.146 | 0.417 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".