Decoding semi-automated title-abstract screening: a retrospective exploration of the review, study, and publication characteristics associated with accurate relevance predictions
Bibliographic record
Abstract
Abstract Background We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening, and explored whether Abstrackr’s predictions varied by review or study-level characteristics. Methods For 16 reviews we screened a 200-record training set in Abstrackr and downloaded the predicted relevance of the remaining records. We retrospectively simulated the liberal-accelerated screening approach: one reviewer screened the records predicted as relevant; a second reviewer screened those predicted as irrelevant and those excluded by the first reviewer. We estimated the time savings and proportion missed compared with dual independent screening. For reviews with pairwise meta-analyses, we evaluated changes to the pooled effects after removing the missed studies. We explored whether the tool’s predictions varied by review and study-level characteristics using Fisher’s Exact and unpaired t-tests. Results Using the ML-assisted liberal-accelerated approach, we wrongly excluded 0 to 3 (0 to 14%) records but saved a median (IQR) 26 (33) hours of screening time. Removing missed studies from meta-analyses did not alter the reviews’ conclusions. Of 802 records in the final reports, 87% were correctly predicted as relevant. The correctness of the predictions did not differ by review (systematic or rapid, P=0.37) or intervention type (simple or complex, P=0.47). The predictions were more often correct in reviews with multiple (89%) vs. single (83%) research questions (P=0.01), or that included only trials (95%) vs. multiple designs (86%) (P=0.003). At the study level, trials (91%), mixed methods (100%), and qualitative (93%) studies were more often correctly predicted as relevant compared with observational studies (79%) or reviews (83%) (P=0.0006). Studies at high or unclear (88%) vs. low risk of bias (80%) (P=0.039), and those published more recently (mean (SD) 2008 (7) vs. 2006 (10), P=0.02) were more often correctly predicted as relevant. Conclusion Our screening approach saved time and may be suitable in conditions where the limited risk of missing relevant records is acceptable. ML-assisted screening may be most trustworthy for reviews that seek to include only trials. Several of our findings are paradoxical, and require further study to fully understand the tasks to which ML-assisted screening is best suited.
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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 | 0.267 | 0.695 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".