Of rats and women: preclinical insights into the nature of female sexual desire
Bibliographic record
Abstract
This review examines the breadth and utility of preclinical models of female sexual desire in rats. Desire is inferred by certain measures, such as solicitations, rates of pacing (in which female rats control the initiation and rate of copulatory contact with male rats), and the strength of operant behavior aimed at acquiring sex partners. Current research reveals a complex interplay between sensory stimulation and sexual reward that shapes the strength and type of sexual responding, and that crystallizes the features of desired partners in female rats. The manner in which steroid hormones and sexual experience alter brain neurochemistry to prime the activation of these pathways is a major focus of this work. In contrast to more traditional assessments of lordosis, these models have predictive validity in terms of human female sexual function and dysfunction: for example, drugs that increase solicitations in female rats, such as the melanocortin agonist bremelanotide, are now being shown in clinical trials to increase subjective sexual desire and the initiation of sexual activity in both pre- and post-menopausal women with hypoactive desire disorders. Because rat models offer the ability to conduct experiments that are considered unethical in humans, they will become increasingly important in the development of pharmacological treatments for sexual dysfunctions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".