Fully Automated Scholarly Search for Biomedical Systematic Literature Reviews
Bibliographic record
Abstract
Biomedical Systematic Literature Reviews (SLRs) play a fundamental role in evidence-informed healthcare and can serve as actionable insights for researchers and policy-making organizations in the field. In this paper, we focus on the phase of ‘study search’ in conducting SLRs, i.e., the process of organizing a comprehensive search via biomedical databases, such as PubMed, in order to obtain all the relevant articles on a certain topic of interest.We introduce FASS-BSLR, a dataset and a benchmark suite to facilitate the development and evaluation of fully automated techniques for study search.We also provide and analyze a set of basic methods along with a number of generative models and report the experiment’s results over the introduced dataset.We introduce a simple but effective model based on the recent transformer-based generative model, ChatGPT, for generating Boolean queries over PubMed. Through different experiments, we illustrate that this model is more effective than basic search models, keyword search over PubMed, and existing methods for crafting Boolean queries using ChatGPT. We show that the introduced model is even more effective than manual queries in terms of Precision, Recall, NDCG, and MAP at positions 10 and 100, but falls short of the recall that manual queries achieve at position 1000. We also report the retrieval performance of different models when a number of relevant articles have been provided as seed documents. We demonstrate that, when three documents are used as seed articles, the introduced model outperforms manual queries in all metrics except Recall@1000, on which its performance is comparable to the performance attained by manual queries.
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 | 0.019 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".