Bibliographic record
Abstract
Pre-eclampsia is a potentially life-threatening disease of women during pregnancy leading to hypertension and proteinuria. It affects 1 in 15 pregnancies but, despite intense research efforts, the cause of the disease remains mysterious. Because pre-eclampsia only occurs during pregnancy and its symptoms resolve after delivery, factors from the placenta are thought to be involved. The role of the placenta could be production of 'abnormal' factors that initiate widespread inflammation and vaso-constriction. Alternatively, because the placenta normally contributes to maternal cardiovascular adaptations of pregnancy, it may be that normal placental functions fail in pre-eclampsia or that susceptibilities in the mother to hypertensive, vascular and/or renal disease prevent the appropriate normal responses to them. The potential contributions of both maternal and fetal genes to the onset of the disease have complicated the genetic analysis of the disease in humans. Recent studies have identified strains of transgenic and mutant mice that develop the hallmark features of pre-eclampsia-like disease - gestational hypertension, proteinuria and kidney lesions (glomerulosclerosis). Comparison of three different mouse models suggests that pre-eclampsia can be initiated by at least three independent mechanisms: pre-existing borderline maternal hypertension that is exacerbated by pregnancy (BPH/5 strain of mice), elevated levels of the vasoconstrictor angiotensin II in the maternal circulation by placental over-production of renin (renin/angiotensinogen transgenic mice), and placental pathology (p57Kip2 mutant mice). These findings imply that the pathogenesis of pre-eclampsia cannot be explained by a single mechanism. Therefore, segregation of the human disease into different subtypes may be a key first step in identifying genetic risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".