A Safety Assessment of Tumor Necrosis Factor Antagonists During Pregnancy: A Review of the Food and Drug Administration Database
Bibliographic record
Abstract
OBJECTIVE: To present any congenital anomalies with respect to tumor necrosis factor (TNF) antagonists reported to the US Food and Drug Administration (FDA) to determine if there are common findings. METHODS: A review of the FDA database of reported adverse events with etanercept, infliximab, and adalimumab from 1999 through December of 2005 was performed. Key words for congenital anomalies were employed as search tools. Duplicate reports were eliminated. Any concomitant medicines were recorded. RESULTS: Our review of > 120,000 adverse events revealed a total of 61 congenital anomalies in 41 children born to mothers taking a TNF antagonist. Of these mothers, 22 took etanercept and 19 took infliximab. There were no reports in women taking adalimumab. The most common reported congenital anomaly was some form of heart defect. Twenty-four of the 41 (59%) children had one or more congenital anomalies that are part of vertebral abnormalities, anal atresia, cardiac defect, tracheoesophageal, renal, and limp abnormalities (VACTERL) association. There were 34 specific types of congenital anomalies in total, and 19 (56%) of those are part of the VACTERL spectrum. Nine of these 19 (47%) types of VACTERL anomalies were observed statistically significantly more than historical controls (p < 0.01); in 4 of these 9 the p value was < or = 0.0001. Thirteen (32%) of the children had more than one congenital anomaly; 7 of these 13 children had 2 defects that are part of the VACTERL spectrum. However, only 1 child was diagnosed with VACTERL. In 24/41 cases (59%) the mother was taking no other concomitant medications. CONCLUSION: A seemingly high number of congenital anomalies that are part of the VACTERL spectrum have been reported. These congenital anomalies are occurring at a rate higher than historical controls. This commonality raises concerns of a possible causative effect of the TNF antagonists.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 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".