Biomarkers of spontaneous preterm birth: a systematic review of studies using multiplex analysis
Bibliographic record
Abstract
OBJECTIVE: Despite decades of research on risk indicators of spontaneous preterm birth (PTB), reliable biomarkers are still not available to screen or diagnose high-risk pregnancies. Several biomarkers in maternal and fetal compartments have been mechanistically linked to PTB, but none of them are reliable predictors of pregnancy outcome. This systematic review was conducted to synthesize the knowledge on PTB biomarkers identified using multiplex analysis. MATERIALS AND METHODS: Three electronic databases (PubMed, EMBASE and Web of Science) were searched for studies in any language reporting the use of multiplex assays for maternal biomarkers associated with PTB published from January 2005 to March 2014. RESULTS: Retrieved citations (3631) were screened, and relevant studies (33) were selected for full-text reading. Ten studies were included in the review. Forty-two PTB-related proteins were reported, and RANTES and IL-10 (three studies) followed by MIP-1β, GM-CSF, Eotaxin, and TNF-RI (two studies) were reported more than once in maternal serum. However, results could not be combined due to heterogeneity in type of sample, study population, assay, and analysis methods. CONCLUSION: By this systematic review, we conclude that multiplex assays are a potential technological advancement for identifying biomarkers of PTB, although no single or combination of biomarkers could be identified to predict PTB risk.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".