One Health: Circadian Medicine Benefits Both Non-human Animals and Humans Alike
Bibliographic record
Abstract
Circadian biology's impact on human physical health and its role in disease development and progression is widely recognized. The forefront of circadian rhythm research now focuses on translational applications to clinical medicine, aiming to enhance disease diagnosis, prognosis, and treatment responses. However, the field of circadian medicine has predominantly concentrated on human healthcare, neglecting its potential for transformative applications in veterinary medicine, thereby overlooking opportunities to improve non-human animal health and welfare. This review consists of three main sections. The first section focuses on the translational potential of circadian medicine into current industry practices of agricultural animals, with a particular emphasis on horses, broiler chickens, and laying hens. The second section delves into the potential applications of circadian medicine in small animal veterinary care, primarily focusing on our companion animals, namely dogs and cats. The final section explores emerging frontiers in circadian medicine, encompassing aquaculture, veterinary hospital care, and non-human animal welfare and concludes with the integration of One Health principles. In summary, circadian medicine represents a highly promising field of medicine that holds the potential to significantly enhance the clinical care and overall health of all animals, extending its impact beyond human healthcare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".