PREECLAMPSIA AND CEREBRAL PALSY IN LOW-BIRTH-WEIGHT AND PRETERM INFANTS: IMPLICATIONS FOR THE CURRENT ?ISCHEMIC MODEL? OF PREECLAMPSIA1*
Bibliographic record
Abstract
OBJECTIVE: One of the prevailing hypotheses for the pathogenesis of preeclampsia is the "ischemic model." It assumes that reduced uteroplacental perfusion is the primary step and the point of convergence of diverse pathogenic processes in the development of preeclampsia. One might expect a fetus under such "ischemic conditions" to be at an increased risk of later development of cerebral palsy (CP). The objective of this study was to test the hypothesis that maternal preeclampsia increases the risk of CP in preterm and low-birth-weight infants. METHODS: A meta-analysis was performed based on published articles identified by searching computerized databases (MEDLINE, EMBASE, CINAHL, Current Contents, Biological Abstracts, and Dissertation Abstracts) from 1966 through 1999. Ten observational studies on the association between preeclampsia and CP were identified based on prespecified inclusion criteria. Two independent reviewers extracted data and assessed the methodological quality of eligible articles. Odds ratios (OR) of CP for preeclampsia from individual studies were pooled. MAIN OUTCOME MEASURE: Cerebral palsy. RESULTS: In case-control studies, preeclampsia was associated with a statistically significant decreased risk of CP [pooled adjusted OR, 0.50; 95% confidence interval (CI), 0.33-0.81; p < 0.01). In cohort studies, preeclampsia was associated with a nonstatistically significant reduced risk of CP (pooled OR, 0.91; 95% CI, 0.35-2.41; p > 0.05). CONCLUSIONS: Preeclampsia may be associated with a decreased risk of CP in preterm and low-birth-weight infants. This challenges the currently held belief that reduced uteroplacental perfusion is the unique pathophysiological process in 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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".