Biological plausibility in environmental health systematic reviews: a GRADE concept paper
Bibliographic record
Abstract
BACKGROUND: "Biological plausibility" is a concept frequently referred to in environmental and public health when researchers are evaluating how confident they are in the results and inferences of a study or evidence review. Biological plausibility is not, however, a domain of one of the most widely-used approaches for assessing the certainty of evidence (CoE) which underpins the findings of a systematic review, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) CoE Framework. Whether the omission of biological plausibility is a potential limitation of the GRADE CoE Framework is a topic that is regularly discussed, especially in the context of environmental health systematic reviews. OBJECTIVES: We analyse how the concept of "biological plausibility", as applied in the context of assessing certainty of the evidence that supports the findings of a systematic review, is accommodated under the processes of systematic review and the existing GRADE domains. RESULTS AND DISCUSSION: We argue that "biological plausibility" is a concept which primarily comes into play when direct evidence about the effects of an exposure on a population of concern (usually humans) is absent, at high risk of bias, is inconsistent, or limited in other ways. In such circumstances, researchers look toward evidence from other study designs in order to draw conclusions. In this respect, we can consider experimental animal and in vitro evidence as "surrogates" for the target populations, exposures, comparators and outcomes of actual interest. Through discussion of 10 examples of experimental surrogates, we propose that the concept of biological plausibility consists of two principal aspects: a "generalisability aspect" and a "mechanistic aspect". The "generalisability aspect" concerns the validity of inferences from experimental models to human scenarios, and asks the same question as does the assessment of external validity or indirectness in systematic reviews. The "mechanistic aspect" concerns certainty in knowledge of biological mechanisms and would inform judgements of indirectness under GRADE, and thus the overall CoE. While both aspects are accommodated under the indirectness domain of the GRADE CoE Framework, further research is needed to determine how to use knowledge of biological mechanisms in the assessment of indirectness of the evidence in systematic reviews.
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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 | 0.486 | 0.797 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".