Searching for grey literature for systematic reviews: challenges and benefits
Bibliographic record
Abstract
There is ongoing interest in including grey literature in systematic reviews. Including grey literature can broaden the scope to more relevant studies, thereby providing a more complete view of available evidence. Searching for grey literature can be challenging despite greater access through the Internet, search engines and online bibliographic databases. There are a number of publications that list sources for finding grey literature in systematic reviews. However, there is scant information about how searches for grey literature are executed and how it is included in the review process. This level of detail is important to ensure that reviews follow explicit methodology to be systematic, transparent and reproducible. The purpose of this paper is to provide a detailed account of one systematic review team's experience in searching for grey literature and including it throughout the review. We provide a brief overview of grey literature before describing our search and review approach. We also discuss the benefits and challenges of including grey literature in our systematic review, as well as the strengths and limitations to our approach. Detailed information about incorporating grey literature in reviews is important in advancing methodology as review teams adapt and build upon the approaches described.
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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 | 0.784 | 0.917 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.055 | 0.054 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.033 | 0.065 |
| Open science | 0.012 | 0.033 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".