Guidance on assessing the methodological and reporting quality of toxicologically relevant studies: A scoping review
Bibliographic record
Abstract
Assessments of methodological and reporting quality are critical to adequately judging the credibility of a study's conclusions and to gauging its potential reproducibility. To aid those seeking to assess the methodological or reporting quality of studies relevant to toxicology, we conducted a scoping review of the available guidance with respect to four types of studies: in vivo and in vitro, (quantitative) structure-activity relationships ([Q]SARs), physico-chemical, and human observational studies. Our aims were to identify the available guidance in this diverse literature, briefly summarize each document, and distill the common elements of these documents for each study type. In general, we found considerable guidance for in vivo and human studies, but only one paper addressed in vitro studies exclusively. The guidance for (Q)SAR studies and physico-chemical studies was scant but authoritative. There was substantial overlap across guidance documents in the proposed criteria for both methodological and reporting quality. Some guidance documents address toxicology research directly, whereas others address preclinical research generally or clinical research and therefore may not be fully applicable to the toxicology context without some translation. Another challenge is the degree to which assessments of methodological quality in toxicology should focus on risk of bias - as in clinical medicine and healthcare - or be broadened to include other quality measures, such as confirming the identity of test substances prior to exposure. Our review is intended primarily for those in toxicology and risk assessment seeking an entry point into the extensive and diverse literature on methodological and reporting quality applicable to their work.
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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 | 0.586 | 0.825 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.053 | 0.039 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.017 | 0.015 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".