A primer on systematic reviews in toxicology
Bibliographic record
Abstract
Systematic reviews, pioneered in the clinical field, provide a transparent, methodologically rigorous and reproducible means of summarizing the available evidence on a precisely framed research question. Having matured to a well-established approach in many research fields, systematic reviews are receiving increasing attention as a potential tool for answering toxicological questions. In the larger framework of evidence-based toxicology, the advantages and obstacles of, as well as the approaches for, adapting and adopting systematic reviews to toxicology are still being explored. To provide the toxicology community with a starting point for conducting or understanding systematic reviews, we herein summarized available guidance documents from various fields of application. We have elaborated on the systematic review process by breaking it down into ten steps, starting with planning the project, framing the question, and writing and publishing the protocol, and concluding with interpretation and reporting. In addition, we have identified the specific methodological challenges of toxicological questions and have summarized how these can be addressed. Ultimately, this primer is intended to stimulate scientific discussions of the identified issues to fuel the development of toxicology-specific methodology and to encourage the application of systematic review methodology to toxicological issues.
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 | 0.139 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".