Data extraction methods for systematic review (semi)automation: Update of a living systematic review
Bibliographic record
Abstract
Background The reliable and usable (semi) automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted using a mix of open-source and commercial tools. This living review update includes publications up to August 2024 and OpenAlex content up to September 2024. Results 117 publications are included in this review. Of these, 30 (26%) used full texts while the rest used titles and abstracts. A total of 112 (96%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 53 (45%), and code from 49 (42%) publications. Nine (8%) implemented publicly available tools. Conclusions This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting other study types. Between review updates, large language models emerged as a new tool for data extraction. While facilitating access to automated extraction, they showed a trend of decreasing quality of results reporting, especially quantitative results such as recall and lower reproducibility of results. Compared with the previous update, trends such as transition to relation extraction and sharing of code and datasets stayed similar.
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 | 0.365 | 0.646 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.065 | 0.050 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".