Integration of existing systematic reviews into new reviews: identification of guidance needs
Bibliographic record
Abstract
BACKGROUND: An exponential increase in the number of systematic reviews published, and constrained resources for new reviews, means that there is an urgent need for guidance on explicitly and transparently integrating existing reviews into new systematic reviews. The objectives of this paper are: 1) to identify areas where existing guidance may be adopted or adapted, and 2) to suggest areas for future guidance development. METHODS: We searched documents and websites from healthcare focused systematic review organizations to identify and, where available, to summarize relevant guidance on the use of existing systematic reviews. We conducted informational interviews with members of Evidence-based Practice Centers (EPCs) to gather experiences in integrating existing systematic reviews, including common issues and challenges, as well as potential solutions. RESULTS: There was consensus among systematic review organizations and the EPCs about some aspects of incorporating existing systematic reviews into new reviews. Current guidance may be used in assessing the relevance of prior reviews and in scanning references of prior reviews to identify studies for a new review. However, areas of challenge remain. Areas in need of guidance include how to synthesize, grade the strength of, and present bodies of evidence composed of primary studies and existing systematic reviews. For instance, empiric evidence is needed regarding how to quality check data abstraction and when and how to use study-level risk of bias assessments from prior reviews. CONCLUSIONS: There remain areas of uncertainty for how to integrate existing systematic reviews into new reviews. Methods research and consensus processes among systematic review organizations are needed to develop guidance to address these challenges.
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 | 0.796 | 0.932 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.057 | 0.043 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.039 | 0.067 |
| Open science | 0.015 | 0.031 |
| Research integrity | 0.022 | 0.024 |
| 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".