Pharmacotherapeutics in clinical ethology: treatment efficacy, clinical pathology and outcome
Bibliographic record
Abstract
Abstract The use of psychopharmaceutical agents is a core aspect of treatment in veterinary behavioural medicine. Psychotropic medication use has shifted the focus of treatment from purely behavioural and environmental interventions to a multi-modal approach. Objective measures of efficacy are required for the licensing of medication. Pharmacotherapeutics have come to encompass supplements and diets, in addition to prescription medications. The first part of this paper examines the efficacy of medications, supplements and diets used in behavioural medicine. Foci include the role of evolution in the types of behavioural concerns reported, the importance of defining abnormal or pathological behaviour, use of terminology that supports stratified mechanistic diagnoses aid in understanding presentation and response clusters, and rational use of medication to relieve emotional, mental and behavioural suffering, given these diagnoses and clusters. The second part of this paper examines the extent to which variation in patient response to medication can enlighten us about mechanisms and outcomes of distress using a series of 3 patient populations who are the focus of studies on separation anxiety and noise reactivity. This response surface approach can be useful for understanding differences in populations in susceptibility to behavioural pathology and in medication response, and may suggest new avenues for drug development and application.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".