Status of animal experimentation in nutrition and dietetic research: Policies of 100 leading journals and new approach methodologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 NAMs were extracted from a range of established sources. 9.6% (n = 8) of journals state they do not publish animal-based studies; 4.8% (n = 4) consider animal studies with qualifications, whereas the remaining 85.5% (n = 71) publish animal studies without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.
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it