Understanding severe maternal morbidity in women pregnant by in vitro fertilization: a population-based cohort study of the Better Outcomes Registry & Network (BORN) Ontario
Bibliographic record
Abstract
Background: The use of in vitro fertilization (IVF) to achieve pregnancy increases the risk of severe maternal morbidity (SMM) -a composite outcome of severe "near miss" complications occurring at deliverycompared with unassisted pregnancy conception.Whether the elevated risk is due to infertility, maternal or paternal factors, or the treatment itself is unclear.It is plausible that the process of controlled ovarian stimulation (COS) used as part of a fresh embryo transfer (ET) cycle may contribute to this risk, mediated by high levels of estrogen and its possible impact on the endometrial lining and the vascular endothelium.this adventure has provided me with the skills and confidence needed to complete this work.She encouraged me to trust in my abilities acquired throughout my master's courses and apply them to my research.Her mentorship truly enriched my experience.I am also indebted to Dr. Deshayne Fell for her counsel during my master's journey.Her kindness and patience as she helped me master SAS software and navigate the world of big data were invaluable.Despite her busy schedule, Dr. Fell always made herself available to me when needed, for which I am deeply grateful.To my committee member, Dr. Olga Basso, I am grateful for her valuable content and statistical expertise throughout this process.I would also like to extend
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".