Assisted reproductive technology or infertility: What underlies adverse outcomes? Lessons from the Massachusetts Outcome Study of Assisted Reproductive Technology
Bibliographic record
Abstract
fertilization and related technologies) is associated with increased adverse pregnancy, neonatal, and childhood developmental outcomes, even in singletons. The comparison group for many had often been a fertile population that conceived without assistance. The Massachusetts Outcome Study of Assisted Reproductive Technology (MOSART) was initiated to define a subfertile population with which to compare ART outcomes. Over more than 10 years, we have used the MOSART database to study pregnancy abnormalities and delivery complications but also to evaluate ongoing health of women, infants, and children. This article will review studies from MOSART in the context of how they compare with those of other investigations. We will present MOSART studies that identified the influence of ART and subfertility/infertility on adverse pregnancy (pregnancy hypertensive disorder, gestational diabetes, placental abnormality) and delivery (preterm birth, low birthweight) outcomes as well as on maternal and child hospitalizations. We will provide evidence that although subfertility/infertility increases the risk of adverse outcomes, there is additional risk associated with the use of ART. Studies exploring the contribution of placental abnormalities as one factor adding to this increased ART-associated risk will be described.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".