Thrombocytopenia and neonatal outcomes among extremely premature infants exposed to maternal hypertension
Bibliographic record
Abstract
Abstract Background Hypertensive disorders of pregnancy (HDP) are associated with neonatal hematological disturbances, such as thrombocytopenia. The association of HDP to platelet counts in the context of extreme prematurity, to trends of platelet counts during neonatal hospitalization, and to frequency of platelet transfusions remain to be explored. Procedure Retrospective study of infants born at less than 29 weeks born between 2015 and 2019. Platelet counts were collected on initial complete blood count, at 2 weeks, 32 weeks post‐menstrual age (PMA), 36 weeks PMA, and closest to discharge. We examined the association between HDP and platelet counts at each time point, frequency of platelet transfusions and intraventricular hemorrhage (IVH) grade 3 or more. Results Total 296 infants were included, 43 exposed to HDP. Infants exposed had lower platelet counts at each time point, as well as a higher prevalence of platelet less than 150 × 109/L on one of the time points (32% vs. 65%, p < .001). Infants exposed to maternal hypertension were more frequently exposed to platelet transfusions (63% vs. 18%, p < .001). Mixed effect model demonstrated an association between HDP and a lower trend in platelet counts at each time point (β = −94 × 103/μl, p < .001). Although initial platelet count was associated with severe IVH, it was not associated to exposure to HDP. Conclusion Premature infants exposed to HDP have a higher prevalence of thrombocytopenia, increased frequency of platelet transfusion, and an altered trend in platelet counts during neonatal hospitalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".