Bibliographic record
Abstract
Systematic reviews provide a structured framework for summarizing the available evidence in a comprehensive, objective, and transparent manner. They inform evidence-based guidelines in medicine, public policy, and more recently, in environmental health and toxicology. Many regulatory agencies have extended and adapted the well-established systematic review methods, initially developed for clinical studies, for their assessment needs. The use of systematic reviews to summarize evidence from existing human, animal, and mechanistic studies can reduce reliance on animal test data in risk assessment and can help avoid unnecessary duplication of animal experiments that have already been conducted. As alternative test methods can be expected to play an increasing role in human health risk assessment in the future, systematic reviews can be particularly helpful in validating these alternatives. The field of evidence-based toxicology has undergone extensive development since its first meeting in 2007 as a result of collaborative efforts among international experts and public health agencies, particularly with respect to the use of mechanistic data and evidence integration. The continued development and wider adoption of systematic review methodology can lead to better 3R implementation. As undertaking a systematic review can be a complex and lengthy process, it is important to understand the main steps involved. Key steps, along with current best practices, are described with references to guidance from organizations with expertise in evidence synthesis. Applications of systematic reviews in clinical, observational, and experimental studies are presented. Finally, software tools available to facilitate and increase the efficiency of completing a systematic review are described.
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 | 0.271 | 0.613 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.013 |
| Bibliometrics | 0.060 | 0.045 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".