Translational pain assessment: could natural animal models be the missing link?
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
Failure of analgesic drugs in clinical development is common. Along with the current "reproducibility crisis" in pain research, this has led some to question the use of animal models. Experimental models tend to comprise genetically homogeneous groups of young, male rodents in restricted and unvarying environments, and pain-producing assays that may not closely mimic the natural condition of interest. In addition, typical experimental outcome measures using thresholds or latencies for withdrawal may not adequately reflect clinical pain phenomena pertinent to human patients. It has been suggested that naturally occurring disease in veterinary patients may provide more valid models for the study of painful disease. Many painful conditions in animals resemble those in people. Like humans, veterinary patients are genetically diverse, often live to old age, and enjoy a complex environment, often the same as their owners. There is increasing interest in the development and validation of outcome measures for detecting pain in veterinary patients; these include objective (eg, locomotor activity monitoring, kinetic evaluation, quantitative sensory testing, and bioimaging) and subjective (eg, pain scales and quality of life scales) measures. Veterinary subject diversity, pathophysiological similarities to humans, and diverse outcome measures could yield better generalizability of findings and improved translation potential, potentially benefiting both humans and animals. The Comparative Oncology Trial Consortium in dogs has pawed the way for translational research, surmounting the challenges inherent in veterinary clinical trials. This review describes numerous conditions similarly applicable to pain research, with potential mutual benefits for human and veterinary clinicians, and their respective patients.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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