The association between preeclampsia and placental trisomy 16 mosaicism
Bibliographic record
Abstract
BACKGROUND: Prenatally diagnosed trisomy 16 mosaicism is associated with the increased risk of poor pregnancy outcome including intrauterine growth restriction, intrauterine death and fetal malformation. While maternal preeclampsia has also been reported in some cases, this has not been systematically evaluated. METHODS: To better define the risk of preeclampsia and the clinical course of preeclampsia in these pregnancies and to identify associated clinical variables, we reviewed 25 cases of prenatally diagnosed trisomy 16 mosaicism for which molecular studies were undertaken and sufficient obstetrical data were present to include/exclude the diagnosis of preeclampsia. RESULTS: Six of 25 (24%) mosaic trisomy 16 cases exhibited preeclampsia as compared to 3 of 44 (7%) matched controls. There were no differences between those mosaic trisomy 16 cases presenting with preeclampsia and those that did not, in terms of the presence/absence of UPD, IUGR, malformation, or trisomy on amniocentesis. Four of the 6 (67%) preeclampsia-associated fetuses were male, compared with only 4 of 19 (21%) (p = 0.06) nonpreeclampsia case fetuses, and three of these also had hypospadias. The levels of trisomy tended to be high in placentas associated with preeclampsia; however very high levels of placental trisomy were also often seen in the absence of preeclampsia. CONCLUSION: As it is impossible to predict which subset of cases is at highest risk, all women receiving a prenatal diagnosis of trisomy 16 mosaicism should be closely monitored for signs of preeclampsia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".