Stability of avoidance behaviour following repeated intermittent treatment with clozapine, olanzapine or D,L-govadine
Bibliographic record
Abstract
Most antipsychotic drugs act as dopamine D2 receptor antagonists within the basal ganglia. These compounds have efficacy in the treatment of positive symptoms of schizophrenia but do not address the cognitive deficits that define this disorder. D,L-Govadine, a recently synthesized tetrahydroprotoberberine, shows efficacy on preclinical tests of antipsychotic action, as well as procognitive properties. We sought to compare D,L-govadine with two atypical antipsychotics, clozapine and olanzapine, on repeated conditioned avoidance responding (CAR), a task that has recently been utilized to model the effects of repeated antipsychotic treatment. After acquisition of two-way avoidance, rats were given D,L-govadine, clozapine, olanzapine or a vehicle control before repeated testing on CAR. Daily sessions were conducted, with 'drug-on' days spaced by a 'drug-off' test day and a rest day, for a total of five drug administrations. Consistent with previous research, the lower dose of olanzapine showed a modest but progressive increase in disruption of avoidance behaviour as observed with many antipsychotics. In contrast, repeated administration of clozapine led to tolerance, and the novel compound D,L-govadine produced a consistent effect across administrations. This stable effect of D,L-govadine on CAR may indicate a desirable preclinical profile for a candidate antipsychotic compound.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".