Circulating Growth Differentiation Factor 15 and Preeclampsia: A Meta-Analysis
Bibliographic record
Abstract
Growth differentiation factor 15 (GDF-15) has been suggested as a potential biomarker of preeclampsia. However, previous studies evaluating circulating GDF-15 in women with preeclampsia showed inconsistent results. A meta-analysis was performed accordingly in this study. Observational studies comparing circulating GDF-15 between women with preeclampsia normal pregnancy were identified by search of electronic databases including PubMed, Embase, Web of Science, Wanfang, and CNKI. The Newcastle-Ottawa Scale (NOS) was used for assessing the quality of the studies. A randomized-effect model incorporating the possible between-study heterogeneity was used to pool the results. Eleven observational studies including 498 women with preeclampsia and 2349 women with normal pregnancy contributed to the meta-analysis. Results showed that compared to controls of women with normal pregnancy at least matched for gestational ages, women with preeclampsia had significantly higher circulating GDF-15 at the diagnosis [standard mean difference (SMD): 0.66, 95% confidence interval (CI): 0.16 to 1.17, p=0.01, I2=93%]. Subgroup analysis showed consistent results in women with preterm and term preeclampsia in Asian and non-Asian women and in studies with different quality scores, which were not statistically significant between subgroups (p for subgroup difference>0.05). Moreover, a higher level of GDF-15 was also found before the diagnosis in women who subsequently developed preeclampsia than those who did not (SMD: 1.32, 95% CI: 0.45 to 2.18, p=0.003, I2=89%). In conclusion, a higher circulating GDF-15 is observed in women with preeclampsia even before the diagnosis of the 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.058 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".