REMS in pregnancy: system perfectly designed to the get the results it gets
Bibliographic record
Abstract
Reproductive drug safety has been a priority for patients and physicians even before the 1960s, when thalidomide—a drug commonly used to alleviate morning sickness—was tied to alarming cases of infants born with phocomelia.1 The Kefauver-Harris Amendment of 1962 prevented thalidomide approval in the USA.1 The legislation also led to immediate reforms in how drugs were approved, but not necessarily how they were prescribed.1 In the decades that followed, processes to regulate safe prescribing lagged. The first reproductive drug safety initiatives were those for isotretinoin (Accutane) and thalidomide: the Accutane Pregnancy Prevention Program (1988), the System for Thalidomide Education and Prescribing Safety (1998) and the System to Manage Accutane-Related Teratogenicity (2002). In response to persistent gaps in these and other drug safety monitoring programmes, the US Food and Drug Administration (FDA) subsequently implemented the Risk Management and Evaluation Strategy (REMS) programme in 2007.2 REMS is a multifaceted programme intended to ensure prescribing and dispensing of specific drugs occur only in situations in which the potential benefits outweigh the potential risks.2 The best known REMS is iPLEDGE, which aims to regulate isotretinoin in pregnancy. Each REMS has key components, including medication guides, product inserts and communication plans, informing healthcare providers and professional societies about drug harms. Additional components called elements to assure safe use (ETASU) apply to drugs with the most significant safety concerns, and include training and certification programmes for physicians and pharmacists, laboratory monitoring, creation of drug registries and restrictions on distribution (eg, hospitals and infusion clinics). The REMS programme was also intended to provide a means of evaluating the efficacy and efficiency of drug safety monitoring programmes. Today, 57 active REMS programmes are approved by the FDA, 10 of them pertaining to drugs in pregnancy (table 1).3 It is important to note …
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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 | 0.050 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.045 | 0.048 |
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".