Comparison of the effects of adrafinil, propentofylline, and nicergoline on behavior in aged dogs
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of adrafinil, propentofylline, and nicergoline for enhancing behavior of aged dogs. ANIMALS: 36 Beagles between 9 and 16 years old. PROCEDURE: Dogs were randomly assigned to receive adrafinil (20 mg/kg of body weight, PO, q 24 h; n = 12), propentofylline (5 mg/kg, PO, q 12 h; 12), or nicergoline (0.5 mg/kg, PO, q 24 h; 12) for 33 days. Baseline behaviors in an open field and in kennels (home cage) were recorded before treatment. After treatment, behaviors in the open field were recorded 2 hours after drug administration on days 2, 15, and 28, and 10 hours after administration on days 7, 20, and 33. Behaviors in the home cage were recorded 2 and 7 hours after drug administration on days 4, 17, and 30. RESULTS: Treatment with adrafinil resulted in a significant increase in locomotion in each of the open-field tests and an increase in locomotion in the home cage. This latter increase was smaller and more variable than that in the open field. Locomotion was not affected by treatment with propentofylline or nicergoline. In the open field, sniffing decreased over time in all 3 groups, but the largest decline was observed in the propentofylline group. CONCLUSIONS AND CLINICAL RELEVANCE: Treatment with adrafinil may improve the quality of life of aged dogs by increasing exploratory behavior and alertness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".