Detection and quantitation of non-steroidal anti-inflammatory drug use close to the time of birth using umbilical cord tissue
Bibliographic record
Abstract
BACKGROUND: Nonsteroidal anti-inflammatory drugs are contraindicated in the third trimester of pregnancy due to negative effects including alteration of uteroplacental blood flow, premature ductus arteriosus closure, and adverse effects on the fetal kidney. However, many women are unaware of these risks, and commonly report their use in pregnancy. We aimed to determine if umbilical cord was a reliable matrix for detecting NSAID use, determine incidence of use close to labour, and uncover associations with obstetric/neonatal outcomes. METHODS: We developed a UHPLC-MS/MS method to simultaneously detect diclofenac, ibuprofen, indomethacin, naproxen, and salicylic acid in plasma and umbilical cord lysate. Using this method, we screened 380 lysates to determine the prevalence of NSAID use. Results were compared to the clinical outcomes in pregnancy using ICD9/10 chart codes (n = 21). RESULTS: The UHPLC-MS/MS method has excellent linearity, accuracy, and precision in solvent and plasma, but lower sensitivity in umbilical cord lysate. We report a 3 % rate of NSAID ingestion within days of labour - the pharmacokinetically-determined window for active ingestion. There were no significant differences observed for maternal, obstetric, or neonatal outcomes between the NSAID positive group (n = 11) and NSAID negative group (n = 369). CONCLUSIONS: fertilisation and prevent pre-eclampsia indicates future work should focus on determining safe dosages of NSAIDs and the correct therapeutic window in pregnancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 0.001 |
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".