Artificial Intelligence Search Tools for Evidence Synthesis: Comparative Analysis and Implementation Recommendations
Bibliographic record
Abstract
To inform implementation recommendations for novel or emerging technologies, Research Information Services at Canada's Drug Agency conducted a multimodal research project involving a literature review, a retrospective comparative analysis, and a focus group on 3 Artificial Intelligence (AI) or automation tools for information retrieval (AI search tools): Lens.org, SpiderCite, and Microsoft Copilot. For the comparative analysis, the customary information retrieval practices used at Canada's Drug Agency served as our reference standard for comparison, and we used the eligible studies of 7 completed projects to measure tool performance. For searches conducted with our usual practice approaches and with each of the 3 tools, we calculated sensitivity/recall, number needed to read (NNR), time to search and screen, unique contributions, and the likely impact of the unique contributions on the projects' findings. Our investigation confirmed that AI search tools have inconsistent and variable performance for the range of information retrieval tasks performed at Canada's Drug Agency. Implementation recommendations from this study informed a "fit for purpose" approach where Information Specialists leverage AI search tools for specific tasks or project types.
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 | 0.543 | 0.794 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.058 | 0.043 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".