Pregnancy outcomes after thyroid cancer
Bibliographic record
Abstract
Background Thyroid cancer is one of the most common cancers in women of reproductive age. Our purpose was to evaluate the association between thyroid cancer and maternal and neonatal outcomes of pregnancy. Methods We conducted a retrospective cohort study using the Healthcare Cost and Utilization Project, Nationwide Inpatient Sample (HCUP-NIS) database from the US. A cohort consisting of women who delivered between 1999 and 2014 was created. Multivariate logistic regression, controlling for baseline maternal characteristics, was used to compare pregnancy complications and neonatal outcomes of pregnant women with thyroid cancer [International Classification of Diseases, ninth edition (ICD-9) code 193] diagnosed before or during pregnancy with those of the obstetric population without thyroid cancer. Results The study included 14,513,587 pregnant women, of which 581 women had a diagnosis of thyroid cancer (4/100,000). During the observation period, there was an upward trend in the prevalence of thyroid cancer among pregnant women, though not statistically significant (P = 0.147). Women with thyroid cancer were more likely to be Caucasian, belong to a higher income quartile, have private insurance, to be discharged from an urban teaching hospital and to have pre-gestational hypertension. Women with thyroid cancer had a greater chance of delivering vaginally, requiring transfusion of blood and developing venous thromboembolism (VTE). Neonates of mothers with thyroid cancer were not found to be at increased risk for the adverse neonatal outcomes examined, specifically, congenital malformations, intrauterine growth restriction, fetal death and preterm labor. Conclusion Pregnancies complicated by thyroid cancer have higher incidences of VTE and need for transfusions, with comparable overall newborn outcomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".