Status of animal experimentation in nutrition and dietetic research: Policies of 100 leading journals and new approach methodologies
Bibliographic record
Abstract
Given animal research is challenged with inadequacies, e.g., animal-to-human knowledge translation, ethical considerations, and cost:benefit, new approach methodologies (NAMs) have been proposed as a replacement. With reference to the field of nutrition and dietetics, our aim was to examine the policies of its leading journals regarding human-based vs. traditional animal-based research; and to explore emerging NAMs that provide alternatives to animal experimentation. We reviewed 100 leading journals from an established database (SCImago Journal Rankings) in the nutrition and dietetics category for the year 2022. Eighty-three journals met the inclusion criteria. NAMs were extracted from a range of established sources. 9.6% (n = 8) of journals state they do not publish animal-based studies; 4.8% (n = 4) consider animal studies with qualifications, whereas the remaining 85.5% (n = 71) publish animal studies without qualification. Across sources, NAMs commonalities were identified including in vitro, in chemico, and in silico methods; and individual and population-based studies. Of leading nutrition/dietetic journals, relatively few have shifted to strictly non-animal methods. Greater attention to the increasing range of NAMs may not only reduce the need for animal research in the field, but may provide superior human-relevant outcomes. Studies are needed to establish their potential superiority.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | MetaresearchScholarly communication Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | MetaresearchScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.335 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.035 | 0.058 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".