Authors’ reply re: The fetal safety of clomiphene citrate: a population based retrospective cohort study
Bibliographic record
Abstract
perspective in pregnancy medication(s) (CC dispensation from two months before conception through the first month of pregnancy), and cost-effective management of pregnancy-related physiological/metabolic aberrations.Interestingly, use of smokeless tobacco during pregnancy had an adverse effect on women's reproductive physiology and the pregnancy outcome.3 Moreover, pooled analyses using patient-specific DNA/RNA/protein-based cell-/molecular-biology research studies involving human subjects along with in vitro cell-/cell-line-based research methodology and animal models of disease would be beneficial in the eventual design of safe CC drug-dose regimens at pre-conception and pregnancy stages during embryonic induction and fetal development.High-throughput chemopredictive drug-dosage pharmacogenetic and/or pharmacokinetic biological assays with standardised drug dosages coupled with gene-epidemiology-based studies with a specific subset of patients from ethnically disparate populations may be incorporated in future designs of cost-effective clinical research protocols so as to provide critical insights into CC exposure and fetal safety in pregnant women of diverse cultural backgrounds.Future multi-centric epidemiological research study protocols using metabolomics, transcriptomics, genomics, and proteomics are warranted to provide unbiased risk assessment in susceptible individuals (e.g.drug responders versus poor responders) and increase our current understanding of the pharmacological basis of CC-mediated adverse drug effects in maternal-fetal medicine, and subsequently aid in the identification of predictive and/or prognostic biomarkers in the ever-expanding reproductive medicine field worldwide.
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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 | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".