Incidence and outcomes of women with non‐<scp>H</scp>odgkin's lymphoma in pregnancy: A population‐based study on 7.9 million births
Bibliographic record
Abstract
AIM: Non-Hodgkin's lymphoma (NHL) is a rare malignancy that can affect women of all ages. The purpose of our study was to estimate the incidence, maternal and fetal outcomes of pregnancy-associated non-Hodgkin's lymphoma (PANHL). MATERIAL AND METHODS: We conducted a population-based cohort study on all births identified in the Healthcare Cost and Utilization Project - Nationwide Inpatient Sample from 2003 to 2011. Disease incidence was calculated and logistic regression was used to estimate the adjusted effect of NHL on maternal and fetal outcomes. RESULTS: Of 7,917,453 births, there were 427 cases of PANHL for an overall incidence of 5.39 per 100,000 births, increasing from 4.44 per 100,000 births to 7.17 per 100,000 births over the 9-year period. Relative to controls, PANHL was more common among Caucasians and women aged 25-34 years. Non-specified PANHL was most commonly coded in >81% of cases, with mycosis fungoides and Burkitt's lymphoma being the other two most common. After adjusting for baseline characteristics, women with PANHL were more likely to have pre-eclampsia, odds ratio (OR) 1.57 (95% confidence interval [CI] 1.06-2.32), cesarean section, OR 1.37 (95%CI 1.13-1.67), preterm births OR 2.50 (95%CI 1.94-3.22), postpartum blood transfusions, OR 2.73 (2.10-3.55), and infectious morbidity, OR 2.81 (95%CI 1.16-6.79). Maternal and fetal mortality rates were significantly increased among women with PANHL. CONCLUSION: The incidence of PANHL is increasing and is associated with an increased risk of maternal and neonatal morbidity and mortality, and as such, women with PANHL may best be managed in specialized centers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".