A primer on systematic reviews in toxicology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.124 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.059 | 0.013 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.011 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.015 |
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 it