Bibliographic record
Abstract
Despite widespread accessibility to prenatal care, little is known on the mechanisms initiating early maternal adaptation to pregnancy. Moreover, preeclampsia and intrauterine growth retardation remain the most frequent and serious complications of pregnancy. Recent studies, both in humans and in laboratory animals, have shown that very early events in gestation may be important determinants for the continuation of healthy pregnancy. Certain of these early adaptations appear to be linked to the corpus luteum of pregnancy, as ovarian steroid hormones (especially progesterone) would set the basic hemodynamic conditions, more specifically, generalized vasodilation. This new hemodynamic setup initiates a vicious cycle in which the renin - angiotensin - aldosterone system is activated, together with the resetting of the control of antidiuretic hormone secretion relative to plasma osmolality. This leads to a gradual and substantial increase in plasma volume and a parallel increase in cardiac function (both heart rate and stroke volume) with the goal of maintaining blood pressure in the face of the generalised vasodilation. This includes the creation of a functional arterio-venous shunt represented by the utero-placental circulation. By the end of the first trimester, the decrease in peripheral vascular resistance is marked relative to the increase in cardiac output, resulting in a significant decrease in blood pressure that will be maintained until the third trimester. It is proposed that in preeclampsia, these very early events (vasodilation - increased plasma volume) fail to occur, resulting in an absence of the usual decrease in blood pressure, which is normally seen in the second trimester of pregnancy, and hypertension in the third trimester. Experimental animals, especially the rat, are suitable models to study this early maternal adaptation to pregnancy, since both endocrine and hemodynamic changes appear to be similar to humans.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".