'Grey literature' in evidence syntheses on Environmental Health & Toxicology (EHT) topics
Bibliographic record
Abstract
When conducting evidence syntheses on healthcare topics, it is recommended to search both journal publications (typically via databases such as Medline or EMBASE) and unpublished sources (‘grey literature’), to identify all of the evidence pertinent to answering a question of interest. The methods for conducting systematic reviews and evidence syntheses on topics other than healthcare are often adapted from the Cochrane Handbook’s methods. The Conduct of Systematic Reviews in Toxicology and Environmental Health Research (COSTER) recommendations cover the key practices for systematic reviews in toxicology and environmental health research. Similar to the Cochrane Handbook, COSTER recommends searching key databases (Recommendation 2.1), searching sources of grey literature (Recommendation 2.2), checking reference lists (Recommendation 2.4), and contacting relevant individuals and organisations (Recommendation 2.5).{Whaley, 2020 #6} Despite the grey literature searching recommendations from COSTER, it is not clear what reviewers conducting evidence syntheses on environmental and health topics (EHT) consider to be the relevant types and sources of grey literature. Nor is it clear how the reviewers deal with grey literature in their evidence syntheses. To broaden our understanding of these issues, we will therefore conduct a survey to identify: 1. How do those conducting evidence syntheses on EHT topics understand ‘grey literature’ – what types of evidence do they regard as ‘grey literature’? 2. Where do they search for grey literature? 3. How do they deal with grey literature evidence included in their evidence syntheses?
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 | 0.526 | 0.776 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.034 | 0.058 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.017 | 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".