Infertility treatment and postpartum mental illness: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Subfertility and infertility treatment can be stressful experiences, but it is unknown whether each predisposes to postpartum mental illness. We sought to evaluate associations between subfertility or infertility treatment and postpartum mental illness. METHODS: We conducted a population-based cohort study of individuals without pre-existing mental illness who gave birth in Ontario, Canada, from 2006 to 2014, stratified by fertility exposure: subfertility without infertility treatment; noninvasive infertility treatment (intrauterine insemination); invasive infertility treatment (in vitro fertilization); and no reproductive assistance. The primary outcome was mental illness occurring 365 days or sooner after birth (defined as ≥ 2 outpatient visits, an emergency department visit or a hospital admission with a mood, anxiety, psychotic, or substance use disorder, self-harm event or other mental illness). We used multivariable Poisson regression with robust error variance to assess associations between fertility exposure and postpartum mental illness. RESULTS: The study cohort comprised 786 064 births (mean age 30.42 yr, standard deviation 5.30 yr), including 78 283 with subfertility without treatment, 9178 with noninvasive infertility treatment, 9633 with invasive infertility treatment and 688 970 without reproductive assistance. Postpartum mental illness occurred in 60.8 per 1000 births among individuals without reproductive assistance. Relative to individuals without reproductive assistance, those with subfertility had a higher adjusted relative risk of postpartum mental illness (1.14, 95% confidence interval 1.10-1.17), which was similar in noninvasive and invasive infertility treatment groups. INTERPRETATION: Subfertility or infertility treatment conferred a slightly higher risk of postpartum mental illness compared with no reproductive assistance. Further research should elucidate whether the stress of infertility, its treatment or physician selection contributes to this association.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".