Uncontrolled pain: a call for better study design
Bibliographic record
Abstract
Studies assessing animal pain in veterinary research are often performed primarily for the benefit of animals. Frequently, the goal of these studies is to determine whether the analgesic effect of a novel treatment is clinically meaningful, and therefore has the capacity to improve the welfare of treated animals. To determine the treatment effect of a potential analgesic, control groups are necessary to allow comparison. There are negative control groups (where pain is unattenuated) and positive control groups (where pain is attenuated). Arising out of animal welfare concerns, there is growing reluctance to use negative control groups in pain studies. But for studies where pain is experimentally induced, the absence of a negative control group removes the opportunity to demonstrate that the study methods could differentiate a positive control intervention from doing nothing at all. For studies that are controlled by a single comparison group, the capacity to distinguish treatment effects from experimental noise is more difficult; especially considering that pain studies often involve small sample sizes, small and variable treatment effects, systematic error and use pain assessment measures that are unreliable. Due to these limitations, and with a focus on farm animals, we argue that many pain studies would be enhanced by the simultaneous inclusion of positive and negative control groups. This would help provide study-specific definitions of pain and pain attenuation, thereby permitting more reliable estimates of treatment effects. Adoption of our suggested refinements could improve animal welfare outcomes for millions of animals globally.
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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 | 0.663 | 0.617 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".