Perinatal Immune Changes to Identify Patients at Risk of Postpartum Preeclampsia
Bibliographic record
Abstract
BACKGROUND: Postpartum preeclampsia (PPPE) is a maternal condition characterized by de novo hypertension in the postpartum period with end-organ damage. We investigated the use of perinatal immune changes, through routine complete blood count (CBC), to identify high-risk individuals before PPPE development. METHODS: We performed a retrospective matched case-control study of 100 individuals with PPPE, 200 term pregnancies (Ctrl) and 200 antenatal preeclampsia (PE). Detailed demographic, obstetrical, and laboratory data were retrieved from medical records. Statistical analysis was performed using one-way ANOVA, multivariate regression, and paired or unpaired t-tests, as appropriate. RESULTS: Individuals who developed PPPE were significantly older and predominantly Black vs Ctrl and PE. Both PE and PPPE had higher pre-pregnancy BMI and increased personal and family history of hypertension/PE vs Ctrl (P < 0.001). Before delivery, individuals who later developed PPPE, had lower total leukocyte counts vs Ctrl (9.61 vs 10.75 × 109/L, P < 0.05) whereas monocytes percentage was elevated (8.07 vs 7.27%, P < 0.01). Comparing the postpartum/antenatal ratio in-between each condition revealed elevated leukocyte ratio in PPPE vs Ctrl (P < 0.001). The Neutrophils ratio was also increased, whereas lymphocytes and monocytes were decreased in PPPE vs both Ctrl and PE. After adjusting for race, maternal age, pre-pregnancy BMI, personal history of PE/HT, and diabetes mellitus, perinatal immune changes were still significantly associated with PPPE. CONCLUSIONS: Globally, perinatal immune changes were observed in individuals with a seemingly uncomplicated pregnancy prior to the development of PPPE. This strongly supports that such changes could be used to identify high-risk individuals prior to disease onset.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".