Animal models of hormonal contraceptives: Understanding drug‐specific and user‐specific variables
Bibliographic record
Abstract
This manuscript reviews findings from the symposium "Hormonal Contraceptives and the Brain: A Focus on Rodent Models," presented at the 2024 meeting for Steroids and the Nervous System in Turin, Italy. Hormonal contraceptives (HCs) are widely used by over 300 million women globally, yet their neurobiological and behavioral impacts have only recently gained extensive research attention. This review emphasizes the importance of animal models in studying these effects due to the practical and ethical limitations of human studies. By distinguishing drug-specific variables (e.g., dosage, chemical composition, routes of administration) from user-specific variables (lifestyle factors, genetic predispositions), researchers can better understand HC-related outcomes. Here, we emphasize the utility of animal models for uncovering putative mechanisms underlying the effects of HCs observed in human studies. Moreover, the authors reflect on the design of the animal models of HC used in their experiments, past and present. We also discuss current research related to user-specific variables which highlight the vulnerability of adolescents to the adverse effects of HCs, exposure to stressors, and the compounded risks of HC when combined with substances like cannabinoids and nicotine. This review underscores the value of animal models in advancing our understanding of the broader neurobiological and behavioral impacts of HCs in humans. These studies are crucial for developing personalized medicine approaches and optimizing contraceptive regimens to mitigate risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".