Best practices for MRI systematic reviews and meta‐analyses
Bibliographic record
Abstract
As defined by the Cochrane Collaboration, a systematic review is a review of evidence with a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant primary research, and to extract and analyze data from the studies that are included in the review. Meta-analysis is a statistical method to combine the results from primary studies that accounts for sample size and variability to provide a summary measure of the studied outcome. Systematic reviews of diagnostic test accuracy present unique methodological and reporting challenges not present in systematic reviews of interventions. This review provides guidance and further resources highlighting current best practices in methodology and reporting of systematic reviews of diagnostic test accuracy, with a specific focus on challenges and opportunities for MRI imaging. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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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 | 0.242 | 0.565 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.044 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".