Long-Term Outcomes of Developmental Exposure to Fluoxetine: A Review of the Animal Literature
Bibliographic record
Abstract
During and following pregnancy, women are at high risk of experiencing depression, for which fluoxetine (FLX; brand names Prozac, Sarafem, Rapiflux) is the most commonly prescribed treatment. An estimated 1.4-2.1% of pregnant women use this medication, which inhibits the reuptake of serotonin and thereby increases serotonergic activity at the synapse. Serotonin acts as a cue guiding numerous neurodevelopmental processes, and changes in the concentration of serotonin can disrupt normal in utero brain development and organization in humans and other animals, thus providing a mechanism by which maternal intake of FLX might alter neural development and ultimately behaviour. Despite this possibility, long-term alterations of behaviour and the brain have not been well studied in individuals exposed to FLX during pregnancy or soon after birth, perhaps because conducting such studies beyond infancy presents significant challenges. To remedy this problem, many researchers have turned to modelling the effects of developmental FLX exposure in non-human animals, primarily rodents. The body of literature on this topic has expanded considerably over the past several years, yet a comprehensive review is lacking. In order to fill this gap, we have summarized the findings of those studies describing the long-term behavioural and neurophysiological effects of FLX exposure in non-human animals in early development. We also discuss methodological considerations and common shortcomings of research in this area. The precise nature of the long-term effects of developmental FLX exposure remains difficult to specify, as these effects appear to be highly variable and dependent on numerous factors. Overall, however, it is clear that early FLX exposure in non-human animals can alter the development of the brain in ways that are relevant to behaviour in adulthood, decreasing exploration and social interaction, and in some cases altering anxiety- and depression-like behaviours..
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".