Additional file 1 of Evaluating the relationship between citation set size, team size and screening methods used in systematic reviews: a cross-sectional study
Bibliographic record
Abstract
Additional file 1: Supplementary Material 1. Screening Criteria. Supplemental Material 2. Data Extraction Variables. Supplementary Material 3. Number of systematic reviews included in each section of the reported results. Supplemental Material 4. Relationship between initial citation set size (log scale) and using more than 2 screeners for the initial screening level. Dots at 1 (or 0) represent systematic reviews which used (or did not use) more than 2 screeners. The blue curve is a binomial 5-knot restricted cubic spline with a 95% shaded confidence band. The vertical line indicates a citation size of 2500. Supplementary Material 5. Relationship between initial citation size (log scale) and using the gold-standard approach for citation screening at the title/abstract level. Dots at 1 (or 0) represent systematic reviews which used (or did not use) the gold-standard approach. The blue curve is a binomial 5-knot restricted cubic spline with a 95% shaded confidence band. The vertical line indicates a citation size of 2500. Supplementary Material 6. Distribution of Methodology Used during Full text Screening by Terciles Created Using Full Text Citation Size (n = 186)a. Supplementary Material 7. Relationship between full-text citation size (log scale) and using the gold-standard approach to screening at the full text level. Dots at 1 (or 0) represent systematic reviews which used (or did not use) the gold-standard approach. The blue curve is a binomial 5-knot restricted cubic spline with a 95% shaded confidence band. Supplementary Material 8. Methodology Used during Data Extraction by Initial Citation Set Size. Supplementary Material 9. Methodology Used during Data Extraction by Terciles Created using Data Extraction Citation Set Size.
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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 | 0.011 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.784 | 0.063 |
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".