Are biologically meaningful effect sizes a factor in study design? A systematic review of translational chronic variable stress studies
Bibliographic record
Abstract
The design of in vivo studies using laboratory animals is normally guided by the 3Rs: Replacement, Reduction and Refinement. The concept of Reduction is particularly important in the context of estimating sample size; the selected sample size should allow the detection of a predetermined effect size using appropriate statistical tests, but not at the expense of using too many animals. To explore this, we conducted a systematic review of animal studies using chronic variable stress to ask whether the authors used a biologically meaningful effect size to determine the sample size. Only one article in our sample of 385 reported doing this, and most articles did not report a justification for the sample size used. Determining a biologically meaningful effect size is not always straightforward, but all appropriately powered studies based on a biologically meaningful effect size are useful, including studies where the data do not support the hypothesis. Accordingly, we believe the use of biologically meaningful effect sizes is central to decisions about study design and interpretation, and we discuss reasons and ways to promote its use.
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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 | 0.077 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".