Candidate biochemical markers for screening of pre-eclampsia in early pregnancy
Bibliographic record
Abstract
Pre-eclampsia (PE) and other hypertensive disorders of pregnancy (HDP) are a leading cause of adverse outcomes. Their pathophysiology remains elusive, hampering the development of efficient prevention. The onset of HDP and PE and the severity of their clinical manifestations are heterogeneous. The advent of preventive measures, such as low-dose aspirin that targets high-risk women, emphasizes the need of better prediction. Until recently, only environmental information and maternal risk factors were considered, with equivocal predictive value. No validated screening procedures were available to identify at-risk women despite the emergence of Doppler ultrasonography parameters for the uterine artery (e.g., pulsatility index and bilateral notching) and pathophysiological biochemical markers (e.g., angiogenesis, inflammation, and endothelial dysfunction). Owing to its heterogeneity and lack of specific, sensitive markers among those studied so far (>200), PE is unlikely to be detected early by a single predictive parameter. Systematic reviews have concluded that no single test fulfilling World Health Organization criteria for biomarker selection can diagnose/predict a disease. However, by combining antenatal risk factors, clinical parameters, as well as biophysical and biochemical markers into multivariate algorithms, the risk of PE can be estimated with performance levels that could reach clinical utility. Performance characteristics of selected algorithms will be presented and discussed with respect to transferability to different geographic and healthcare environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".