How can we improve the interpretation of systematic reviews?
Bibliographic record
Abstract
A study conducted by Lai and colleagues, published this week in BMC Medicine, suggests that more guidance might be required for interpreting systematic review (SR) results. In the study by Lai and colleagues, positive (or favorable) results were influential in changing participants' prior beliefs about the interventions presented in the systematic review. Other studies have examined the relationship between favorable systematic review results and the publication of systematic reviews. An international registry may decrease the number of unpublished systematic reviews and will hopefully decrease redundancy, increase transparency, and increase collaboration within the SR community. In addition, using guidance from the Preferred Items for Systematic Reviews and Meta-analyses (PRISMA: http://www.prisma-statement.org/) Statement and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE: http://www.gradeworkinggroup.org/) approach can also be used to improve the interpretation of systematic reviews. In this commentary, we highlight important methodological issues related to the conduct and reporting of systematic reviews and also present our own guidance on interpreting systematic reviews. Please see Research article: http://www.biomedcentral.com/1741-7015/9/30/.
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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 | 0.829 | 0.950 |
| Meta-epidemiology (narrow) | 0.013 | 0.014 |
| Meta-epidemiology (broad) | 0.034 | 0.020 |
| Bibliometrics | 0.053 | 0.033 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.045 | 0.050 |
| Open science | 0.020 | 0.018 |
| Research integrity | 0.032 | 0.043 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".