A narrative review of recent tools and innovations toward automating living systematic reviews and evidence syntheses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Living reviews are an increasingly popular research paradigm. The purpose of a 'living' approach is to allow rapid collation, appraisal and synthesis of evolving evidence on an important research topic, enabling timely influence on patient care and public health policy. However, living reviews are time- and resource-intensive. The accumulation of new evidence and the possibility of developments within the review's research topic can introduce unique challenges into the living review workflow. To investigate the potential of software tools to support living systematic or rapid reviews, we present a narrative review informed by an examination of tools contained on the Systematic Review Toolbox website. We identified 11 tools with relevant functionalities and discuss the important features of these tools with respect to different steps of the living review workflow. Four tools (NestedKnowledge, SWIFT-ActiveScreener, DistillerSR, EPPI-Reviewer) covered multiple, successive steps of the review process, and the remaining tools addressed specific components of the workflow, including scoping and protocol formulation, reference retrieval, automated data extraction, write-up and dissemination of data. We identify several ways in which living reviews can be made more efficient and practical. Most of these focus on general workflow management, or automation through artificial intelligence and machine-learning, in the screening process. More sophisticated uses of automation mostly target living rapid reviews to increase the speed of production or evidence maps to broaden the scope of the map. We use a case study to highlight some of the barriers and challenges to incorporating tools into the living review workflow and processes. These include increased workload, the need for organisation, ensuring timely dissemination and challenges related to the development of bespoke automation tools to facilitate the review process. We describe how current end-user tools address these challenges, and which knowledge gaps remain that could be addressed by future tool development. Dedicated web presences for automatic dissemination of in-progress evidence updates, rather than solely relying on peer-reviewed journal publications, help to make the effort of a living evidence synthesis worthwhile. Despite offering basic living review functionalities, existing end-user tools could be further developed to be interoperable with other tools to support multiple workflow steps seamlessly, to address broader automatic evidence retrieval from a larger variety of sources, and to improve dissemination of evidence between review updates.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.399 | 0.794 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.050 | 0.007 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it