Detection and quantitation of non-steroidal anti-inflammatory drug use close to the time of birth using umbilical cord tissue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it