Are animal models relevant to key aspects of human parturition?
Bibliographic record
Abstract
Preterm birth remains the most serious complication of pregnancy and is associated with increased rates of infant death or permanent neurodevelopmental disability. Our understanding of the regulation of parturition remains inadequate. The scientific literature, largely derived from rodent animal models, suggests two major mechanisms regulating the timing of parturition: the withdrawal of the steroid hormone progesterone and a proinflammatory response by the immune system. However, available evidence strongly suggests that parturition in the human has significantly different regulators and mediators from those in most of the animal models. Our objectives are to critically review the data and concepts that have arisen from use of animal models for parturition and to rationalize the use of a new model. Many animal models have contributed to advances in our understanding of the regulation of parturition. However, we suggest that those animals dependent on progesterone withdrawal to initiate parturition clearly have a limitation to their translation to the human. In such models, a linear sequence of events (e.g., luteolysis, progesterone withdrawal, uterine activation, parturition) gives rise to the concept of a "trigger" mechanism. Conversely, we propose that human parturition may arise from the concomitant maturation of several systems in parallel. We have termed this novel concept "modular accumulation of physiological systems" (MAPS). We also emphasize the urgency to determine the precise role of the immune system in the process of parturition in situations other than intrauterine infection. Finally, we accentuate the need to develop a nonprimate animal model whose physiology is more relevant to human parturition. We suggest that the guinea pig displays several key physiological characteristics of gestation that more closely resemble human pregnancy than do currently favored animal models. We conclude that the application of novel concepts and new models are required to advance translational research in parturition.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".