Expectations for the Methodology and Translation of Animal Research: A Survey of the General Public, Medical Students and Animal Researchers in North America
Bibliographic record
Abstract
To determine what are considered acceptable standards for animal research (AR) methodology and translation rate to humans, a validated survey was sent to: a) a sample of the general public, via Sampling Survey International (SSI; Canada), Amazon Mechanical Turk (AMT; USA), a Canadian city festival (CF) and a Canadian children's hospital (CH); b) a sample of medical students (two first-year classes); and c) a sample of scientists (corresponding authors and academic paediatricians). There were 1379 responses from the general public sample (SSI, n = 557; AMT, n = 590; CF, n = 195; CH, n = 102), 205/330 (62%) medical student responses, and 23/323 (7%, too few to report) scientist responses. Asked about methodological quality, most of the general public and medical student respondents expect that: AR is of high quality (e.g. anaesthesia and analgesia are monitored, even overnight, and 'humane' euthanasia, optimal statistical design, comprehensive literature review, randomisation and blinding, are performed), and costs and difficulty are not acceptable justifications for lower quality (e.g. costs of expert consultation, or more laboratory staff). Asked about their expectations of translation to humans (of toxicity, carcinogenicity, teratogenicity and treatment findings), most expect translation more than 60% of the time. If translation occurred less than 20% of the time, a minority disagreed that this would "significantly reduce your support for AR". Medical students were more supportive of AR, even if translation occurred less than 20% of the time. Expectations for AR are much higher than empirical data show to have been achieved.
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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 | 0.248 | 0.302 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".