Phenotypes of maternal vascular malperfusion placental pathology and adverse pregnancy outcomes: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To identify which components of maternal vascular malperfusion (MVM) pathology are associated with adverse pregnancy outcomes and to investigate the morphological phenotypes of MVM placental pathology and their relationship with distinct clinical presentations of pre-eclampsia and/or fetal growth restriction (FGR). DESIGN: Retrospective cohort study. SETTING: Tertiary care hospital in Toronto, Canada. POPULATION: Pregnant individuals with low circulating maternal placental growth factor (PlGF) levels (<100 pg/mL) and placental pathology analysis between March 2017 and December 2019. METHODS: Association between each pathological finding and the outcomes of interest were calculated using the chi-square test. Cluster analysis and logistic regression was used to identify phenotypic clusters, and their association with adverse pregnancy outcomes. Cluster analysis was performed using the K-modes unsupervised clustering algorithm. MAIN OUTCOME MEASURES: weeks of gestation, birthweight <10th percentile (small for gestational age, SGA) and stillbirth. RESULTS: weeks of gestation were: infarction, accelerated villous maturation, distal villous hypoplasia and decidual vasculopathy. Two dominant phenotypic clusters of MVM pathology were identified. The largest cluster (n = 104) was characterised by both reduced placental mass and hypoxic ischaemic injury (infarction and accelerated villous maturation), and was associated with combined pre-eclampsia and SGA. The second dominant cluster (n = 59) was characterised by infarction and accelerated villous maturation alone, and was associated with pre-eclampsia and average birthweight for gestational age. CONCLUSIONS: Patients with placental MVM disease are at high risk of pre-eclampsia and FGR, and distinct pathological findings correlate with different clinical phenotypes, suggestive of distinct subtypes of MVM disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".