The Toxicity Profile of a Single Dose of Paroxetine: An Alternative Approach to Acute Toxicity Testing in the Rat
Bibliographic record
Abstract
In this study we have examined the effect of a single administration of the selective serotonin reuptake inhibitor, paroxetine (120-300 mg kg(-1), orally) in a recently developed rodent model of acute toxicity testing. Reduced body-weight, food consumption, water consumption and body temperature were observed in all paroxetine-treated groups, which were reversible within 7 days. Five days after administration, a dose-dependent increase in red blood cells, haemoglobin and haematocrit was observed with the 3 higher dose levels of paroxetine, which was significant in the 240 and 300 mg kg(-1) treatment groups (P < 0.05). Hyperactivity was apparent in the first 24 hr following treatment, as was evidence of the serotonin syndrome. When the animals were sacrificed (11 days after drug administration), an increase in liver weight was observed in the highest dose. These results are in agreement with those previously observed with paroxetine at the preclinical and clinical levels. They demonstrate that this rodent model, because of its multi-parameter nature, is a useful method for examining the consequences of a single high dose of an antidepressant drug.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".