Bibliographic record
Abstract
Prescription and over-the-counter drug use during pregnancy is necessary for many women today. A study of US and Canadian women found that, on average, 2.3 drugs were used during pregnancy; however, 28% reported using more than 4. For some women, this is because they become pregnant with preexisting conditions that require ongoing or intermittent pharmacotherapy. For others, this is because pregnancy itself can give rise to new medical conditions such as gestational diabetes and preeclampsia. The principal concern of prescribing physicians is whether or not agents will harm the fetus (i.e., have teratogenic effects). This concern rose to prominence primarily as a result of the thalidomide disaster. Marketed for use in morning sickness, thalidomide was found to be a potent teratogen capable of producing a variety of birth defects relating to development. Consequently, determining the teratogenicity of new drugs currently dominates the objectives of pregnancy-relevant experiments conducted throughout drug development. This often comes at the expense of valuable pharmacokinetic (PK) studies, which are seldom performed pre-market. Sex differences in PK parameters have been demonstrated in animals and humans since the 1930s. It is, therefore, not surprising that differences also arise in pregnancy. A wide array of physiological and hormonal changes occur during pregnancy; most begin early in the first trimester and increase linearly until parturition. Physicians lacking adequate PK information typically prescribe the standard adult dose in pregnancy, and this can be either inadequate or excessive depending on a variety of factors. The purpose of this report is to highlight this issue and illustrate how current methods used to obtain PK data in pregnancy are insufficient. The steps that are being taken to address this issue will also be discussed.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".