Performance of a mixed filter to identify relevant studies for mixed studies reviews
Bibliographic record
Abstract
OBJECTIVE: Mixed studies reviews include empirical studies with diverse designs. Given that identifying relevant studies for such reviews is time consuming, a mixed filter was developed. METHODS: The filter was used for six journals from three disciplines. For each journal, database records were coded "empirical" (relevant) when they mentioned a research question or objective, data collection, analysis, and results. We measured precision (proportion of retrieved documents being relevant), sensitivity (proportion of relevant documents retrieved), and specificity (proportion of nonrelevant documents not retrieved). RESULTS: Records were coded with and without the filter, and descriptive statistics were performed, suggesting the mixed filter has high sensitivity.
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 | 0.462 | 0.775 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".