Developing a Model of Nutritional Amenorrhea in Rhesus Monkeys
Bibliographic record
Abstract
Nutritional amenorrhea is defined as cessation of menstrual cycles resulting from a chronic negative energy balance. Although it is agreed that nutritional amenorrhea results from reduced secretion of GnRH, the neuroendocrine mechanisms leading to GnRH inhibition are poorly defined. Because the invasiveness of many neuroendocrine experimental approaches precludes its use in the clinical setting, we set out to establish a model of nutritional amenorrhea in rhesus monkeys. Studies were conducted in four normal-weight and one obese female rhesus monkey. Dietary intake was gradually reduced with the goal of achieving a 15-20% weight reduction. Dietary restriction inhibited ovulation in all animals. The weight loss required to inhibit ovulation ranged from 2-11% in the four normal-weight animals and was achieved with a 23% reduction in dietary intake. The time of initiating reduced food intake to first missed ovulation was 62 +/- 13 d. Greater weight loss (46% reduction) over a longer period (10 months) was required to inhibit ovulation in the obese monkey. The onset of anovulation was not preceded by changes in menstrual cycle length or progesterone secretion. Realimentation initiated ovulation at a weight that approximated the animal's weight at the time of the last ovulatory cycle during dietary restriction. By contrast, caloric intake at the return of ovulation during realimentation was 28% greater. This is the first demonstration that chronic dietary restriction can inhibit ovulation in rhesus monkeys. This model will be useful for studying the neuroendocrine mechanisms involved in diet-induced anovulation in primates.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".