Major malformations after first trimester exposure to aspirin and NSAIDs
Bibliographic record
Abstract
The use of aspirin and other NSAIDs during the first trimester of pregnancy is widespread, despite inconclusive evidence regarding the possible risks for the baby. We present an overview of the current evidence relating to the associations between aspirin or NSAID use during the first trimester of pregnancy and the risk of congenital malformations. We systematically searched Medline, Embase, the Cochrane Library and the reference lists of all relevant articles from 1966 to March 2008 that examined the association between aspirin and NSAID use during the first trimester of pregnancy and the risk of congenital malformations in humans. We analyzed 30 studies that met the predefined inclusion criteria: 22 case-control studies, seven cohort studies and one randomized, controlled trial. There are not enough human data available to assess the effect of high-dose aspirin and NSAIDs in pregnant women, such as those used in the treatment of rheumatoid arthritis, osteoarthritis and pain relief. This review suggests that the exposure to aspirin or NSAIDs during the first trimester of pregnancy is associated with an increased risk of gastroschisis (aspirin), cardiac malformations (NSAIDs) and orofacial malformations (naproxen).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".