Maternal cytokine profiles in second and early third trimester are not predictive of preterm birth
Bibliographic record
Abstract
Previous studies have investigated whether inflammatory cytokines in maternal circulation are associated with preterm birth. However, many have reported inconsistent results, and few have investigated cytokine trends through gestation, particularly with respect to subtypes of preterm birth. We explored levels of 15 inflammatory cytokines and growth factors in plasma and serum collected in the second (17-23 weeks, timepoint 1 (T1)) and third (28-32 weeks, timepoint 2 (T2)) trimesters with respect to subtypes of preterm birth: spontaneous preterm labour (sPTL), preterm premature rupture of membranes (PPROM), and medically indicated preterm birth (mPTB). The change in TNFα levels over time (T2/T1) significantly classified mPTB from term birth with an AUC of 0.79. While elevated sICAM-1 levels were significantly associated with sPTL, sICAM-1 was not an effective biomarker for prediction. While statistical differences in some biomarkers, such as TNFα and sICAM-1 were found, these are likely not clinically meaningful for prediction. These results did not reveal a relationship between spontaneous labour and circulating maternal inflammatory biomarkers, however, do suggest distinct inflammatory profiles between subtypes of preterm birth.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".