Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool
Bibliographic record
Abstract
BACKGROUND: Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We evaluated a machine learning and text mining tool's ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. METHODS: For 75 randomized trials, we manually extracted and verified data for 21 data elements. We uploaded the randomized trials to an online machine learning and text mining tool, and quantified performance by evaluating its ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by the tool. We calculated the median (interquartile range [IQR]) time for manual and semi-automated data extraction, and overall time savings. RESULTS: The tool identified the reporting (reported or not reported) of data elements with median (IQR) 91% (75% to 99%) accuracy. Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88% (83% to 99%) of cases. Among a median (IQR) 90% (86% to 97%) of relevant sentences, pertinent fragments had been highlighted by the tool; exact matches were unreliable (median (IQR) 52% [33% to 73%]). A median 48% of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 h total extraction time across 75 randomized trials). CONCLUSIONS: Using ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool's ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required. PROTOCOL: https://doi.org/10.7939/DVN/RQPJKS.
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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 | 0.690 | 0.856 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".