Low pregnancy-associated plasma protein A level in the first trimester.
Bibliographic record
Abstract
OBJECTIVE: To review the recent evidence behind the association of low levels (ie, below the fifth percentile) of pregnancy-associated plasma protein A (PAPP-A) with adverse perinatal outcomes and to integrate new findings with the recommendations made by the Society of Obstetricians and Gynaecologists of Canada in 2008. QUALITY OF EVIDENCE: A review of recently published articles revealed that current evidence is sparse and mixed for the association of low PAPP-A level with small size for gestational age, preterm delivery, hypertensive disorders of pregnancy, and stillbirth. There is limited evidence that suggests an association between low PAPP-A levels and spontaneous pregnancy loss. Recent studies suggest that low PAPP-A levels are associated with abnormal placentation, which might be the root cause of the adverse perinatal outcomes of interest. MAIN MESSAGE: The evidence behind the association of low PAPP-A levels with adverse perinatal outcomes is both lacking and mixed. However, recent data do suggest an association between low PAPP-A levels and abnormal placentation. This emerging topic currently lacks strong evidence-based guidelines, yet has potential important implications for perinatal outcomes. Collaboration with obstetric specialists regarding pregnant women who have low PAPP-A levels in the context of normal first-trimester aneuploidy screening results might aid clinical decision making about pregnancy and placental surveillance. CONCLUSION: While the clinical meaning of a low PAPP-A level detected in the context of normal fetal aneuploidy screening remains under debate, pregnant patients with such results should be counseled that at present no strong evidence exists to justify an ongoing ultrasound surveillance program.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".