Assisted human reproduction and pregnancy outcomes in Alberta, 2009–2018: a population-based study
Bibliographic record
Abstract
Background: Assisted human reproduction (AHR) can be used to help individuals and couples overcome infertility issues. We sought to describe trends in pregnancies using AHR and to evaluate the impact of AHR on perinatal outcomes in a large population-based cohort in Alberta, Canada. Methods: We linked maternal and child administrative data for all live births occurring July 1, 2009, to Dec. 31, 2018, in Alberta, Canada, for this retrospective study. We identified AHR pregnancies from pharmaceutical claims or codes from the International Classification of Diseases and Related Health Problems (9th or 10th revision). Our main outcome measures were the incidence and temporal trends of live births in AHR pregnancies. We also compared maternal characteristics and perinatal outcomes of AHR and non-AHR pregnancies, and by maternal age group. Results: Of 518 293 live births during the study period, 26 270 (5.1%) were conceived with AHR. The incidence of AHR pregnancies increased from 30.8 per 1000 pregnancies in 2009 to 54.7 per 1000 pregnancies in 2018. Females who used AHR were older (33.9 yr v. 30.1 yr, p < 0.001) and the number of females aged 30–35 years and older than 35 years who delivered following AHR increased over the study period (30–35 yr: 36.9 to 55.3 per 1000 pregnancies; > 35 yr: 79.1 to 95.2 per 1000 pregnancies). The proportion of live births with cesarean delivery (40.5% v. 23.3%, p < 0.001), low birth weight (26.9% v. 7.6%, p < 0.001), congenital malformation (0.5% v. 0.3%, p = 0.002) and admission to the neonatal intensive care unit (25.3% v. 9.7%, p < 0.001) was higher in the AHR group than the non-AHR group. Interpretation: The incidence of live births following AHR pregnancies in Alberta was 5.1% between 2009 and 2018, and increased by 0.26% per year; newborns in the AHR group appeared smaller and showed signs of poorer health. This study provides insights on potential perinatal complications following AHR that may be important when caring for the newborn child.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".