Mode of Conception and Risk of Spontaneous vs. Provider-Initiated Preterm Birth: Population-based cohort study
Bibliographic record
Abstract
Objective: To study the association between mode of conception and risk of preterm birth (PTB), including, spontaneous and provide-initiated PTB. Design: Retrospective cohort study Setting and Population: Population-based cohort included all singleton livebirths and stillbirth in Ontario, Canada, 2006-2014. Methods: Mode of conception comprised (i) subfertility without infertility treatment (N = 68,822); (ii) non-invasive infertility treatment (ovulation induction +/- intrauterine insemination) (N = 9024); or (iii) Invasive infertility treatment (in vitro fertilization [N = 8038) – each compared to births by unassisted conception (N = 646,926). Modified Poisson regression generated risk ratios (RR). Confounding was handled by inverse probability of treatment weighting using a propensity that included maternal demographics and pre-existing conditions. Main Outcome Measure: PTB < 37 completed weeks’ gestation. PTB was further categorized as spontaneous PTB or provider-initiated PTB. Results: PTB occurred among 6.0% of births by unassisted conception, 7.7% with subfertility, 8.0% with non-invasive infertility treatment, and 10.8% following invasive infertility treatment. The RR of provider-initiated PTB was higher in women with subfertility (RR 1.23, 95% CI 1.16-1.31), non-invasive infertility treatment (RR 1.48, 1.29-1.69) and invasive infertility treatment (RR 2.35, 2.09-2.64) – each relative to births by unassisted conception. The corresponding RR for spontaneous PTB were 1.15 (95% CI 1.10-1.19), 1.19 (95% CI 1.09-1.31) and 1.40 (95% CI 1.27-1.53). Conclusions: Subfertility, and receipt of infertility treatment, are each associated with a higher risk of PTB, especially provider-initiated PTB. Strategies are needed to reduce the underlying indications to deliver these women before term.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".