Placental Infarction and Thrombophilia
Bibliographic record
Abstract
OBJECTIVE: To estimate the relative importance of positive maternal thrombophilia testing compared with additional pathological evidence of abnormal placentation with placental infarction. METHODS: We performed a retrospective cohort study over a 10-year period in 180 singleton high-risk pregnancies (delivery at 22-34 6/7 weeks of gestation) that had histologic evidence of placental infarction. The rate of positive maternal tests for antiphospholipid syndrome, factor V Leiden, and prothrombin gene mutation were compared with the rate of detection of one or more gross or histological features of abnormal placentation (impaired placental development or differentiation, maternal vascular underperfusion, fetal vascular underperfusion, chronic inflammation, or intervillous thrombosis). RESULTS: Only 14 of 108 (13.0%) of placentas with documented infarction were associated with a positive maternal thrombophilia result. In contrast, 67 of 108 (62.3%) placentas showed features of abnormal placental development or differentiation and 85 of 108 (78.7%) had evidence of noninfarct-related maternal vascular underperfusion (P<.001). Only four of 108 (3.7%) infarcted placentas had no other pathologic lesions. CONCLUSION: Our data indicate that gross and histologic features of abnormal placentation associate strongly with placental infarction in comparison with maternal thrombophilia tests. LEVEL OF EVIDENCE: II.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".