Perceptions of Pregnancy and Lactation from the Pregnancy and Lactation Autoimmune Network Registry
Bibliographic record
Abstract
OBJECTIVE: The Pregnancy and Lactation Autoimmune Network (PLAN) registry was established to evaluate the concerns of women with autoimmune or inflammatory rheumatic diseases (AIRD) pertaining to pregnancy and lactation. METHODS: The registry was started as a survey of patients with AIRD at a single rheumatology specialty center in November 2016 and included questions regarding fertility, pregnancy, miscarriages, and lactation before and after diagnosis. RESULTS: The study included 154 subjects from the PLAN registry. More than half (52%) of respondents indicated that their diagnosis negatively changed their views on pregnancy and nearly a third (30%) decided not to have children after AIRD diagnosis. Most (66%) women were concerned that medication use during the childbearing process would affect the baby. One-third (34%) indicated their views on breastfeeding negatively changed as a result of their disease diagnosis. The rates and duration of breastfeeding did not differ significantly for babies born before or after the mothers' diagnosis (p = 0.50 and p = 0.21, respectively). Eighteen women in our study avoided breastfeeding or stopped breastfeeding earlier than planned to start a medication (including etanercept, adalimumab, hydroxychloroquine, and certolizumab) they believed to be contraindicated during lactation. The PLAN registry included 19 women who breastfed 22 babies while being exposed to a disease-modifying antirheumatic drug or biologic. None of these 19 women reported a delay in their children's developmental milestones or higher infection rates. CONCLUSION: This study highlights an unmet need in patients with AIRD of childbearing potential for data and education regarding pregnancy and lactation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".