Bibliographic record
Abstract
Problem Preterm birth (PTB), defined as delivery before 37 weeks’ gestation, affected ~29,000 pregnancies in Canada in 2023 and is a leading cause of neonatal morbidity and mortality. Inflammatory mediators and maternal stress hormones have been implicated in the pathways leading to PTB; however, their predictive value as blood biomarkers remains unclear. Evidence also suggests there are fetal sex-specific differences in PTB prevalence, yet few studies have examined fetal sex associations with circulating biomarkers in the context of PTB. The objective of this thesis is to identify associations between birth outcomes, fetal sex, and 1) inflammatory mediators, and 2) cortisol and cortisone levels Method of study Maternal serum was collected between 9-13 weeks’ gestation from Early Risk Assessment (ERA) participants. Maternal plasma was collected between 17-23 weeks’ gestation and serum was collected between 28-32 weeks’ gestation from the All Our Family (AOF) cohort. All participants had either a subsequent spontaneous term birth or spontaneous (s)PTB. Inflammatory mediators including interleukin-6, interleukin-8, interleukin-10, interleukin-1 beta, tumor necrosis factor alpha, and 15-HETE were measured in the ERA samples by multiplex assays or ELISAs. Additionally, ELISAs measured cortisol and cortisone in the ERA and AOF samples. The data were analysed using logistic regression, parametric, and non-parametric statistical tests. General conclusion The inflammatory mediator and steroid levels reported here were associated with sPTB but were not predictive. Similarly, associations between biomarkers and fetal sex were identified but the fetal sex discrepancy observed in sPTB remains unclear. Expanding on the biomarker and demographic data may contribute to training a more robust machine learning model for sPTB prediction. Lastly, quantifying biomarkers throughout gestation and at later gestational ages may also provide a better option for the prediction of sPTB.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".