Model-projected mechanistic bases for sex differences in growth hormone regulation in humans
Bibliographic record
Abstract
Models of physiological systems facilitate rational experimental design, inference, and prediction. A recent construct of regulated growth hormone (GH) secretion interlinks the actions of GH-releasing hormone (GHRH), somatostatin (SRIF), and GH secretagogues (GHS) with GH feedback in the rat (Farhy LS, Veldhuis JD. Am J Physiol Regul Integr Comp Physiol 288: R1649-R1663, 2005). In contrast, no comparable formalism exists to explicate GH dynamics in any other species. The present analyses explore whether a unifying model structure can represent species- and sex-defined distinctions in the human and rodent. The consensus principle that GHRH and GHS synergize in vivo but not in vitro was explicable by assuming that GHS 1) evokes GHRH release from the brain, 2) opposes inhibition by SRIF both in the hypothalamus and on the pituitary gland, and 3) stimulates pituitary GH release directly and additively with GHRH. The gender-selective principle that GH pulses are larger and more irregular in women than men was conferrable by way of 4) higher GHRH potency and 5) greater GHS efficacy. The overall construct predicts GHRH/GHS synergy in the human only in the presence of SRIF when the brain-pituitary nexus is intact, larger and more irregular GH pulses in women, and observed gender differences in feedback by GH and the single and paired actions of GHRH, GHS, and SRIF. The proposed model platform should enhance the framing and interpretation of novel clinical hypotheses and create a basis for interspecies generalization of GH-axis regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 0.000 |
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".