Prenatal allostatic load and preterm birth: A systematic review
Bibliographic record
Abstract
Objective: Allostatic load refers to cumulative neuroendocrine burden and has been postulated to mediate and moderate physiological and psychological stress-related responses. This may have important implications for the risk of preterm birth. This systematic review examines the evidence on the association between prenatal allostatic load and preterm birth. Data sources: A comprehensive search of seven electronic databases was conducted from inception to August 23, 2022 to identify all English-language observational and mixed methods studies examining allostatic load and preterm birth with no year or geographic restrictions. Study eligibility criteria: Studies were included if they measured allostatic load, evaluated as the cumulative effect of any combination of more than one allostatic load biomarker, during pregnancy. Studies must have observed preterm birth, defined as < 37 weeks' gestational age, as a primary or secondary outcome of interest. Study appraisal and synthesis methods: The Quality In Prognosis Studies tool was used to evaluate risk of bias within included studies. A narrative synthesis was conducted to explore potential associations between allostatic load and preterm birth, and sources of heterogeneity. Results: Three prospective cohort studies were identified and revealed mixed evidence for an association between allostatic load and preterm birth. One study reported a statistically significant association while the other two studies reported little to no evidence for an association. Heterogeneity in when and how allostatic load was measured, limitations in study design and cohort socio-demographics may have contributed to the mixed evidence. Conclusions: This review provides insight into key individual-, community-, and study-level characteristics that may influence the association between allostatic load and preterm birth. Knowledge gaps are identified as foci for future research, including heterogeneity in allostatic load biomarkers and allostatic load index algorithms as well as pregnancy-specific considerations for allostatic load measurement. Further investigation of the allostatic load framework in the context of perinatal mental health is needed to advance understandings of maternal, infant, and child health. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020208990, PROSPERO, identifier: CRD42020208990.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".