The Importance of Gender-Neutral Terminology in Risk Evaluation and Mitigation Strategy Programs: A Call to Action
Bibliographic record
Abstract
The use of risk evaluation and mitigation strategy (REMS) programs is frequently required for prescriptions with potentially teratogenic effects, especially in the field of dermatology. Among these REMS programs, the most well-known example is isotretinoin, an oral retinoid that uses the iPLEDGE system. iPLEDGE has strict regulations and a lengthy approval process, and until recently, patients were grouped into 3 categories: male, female, or female of reproductive potential. This strict grouping has posed problems in the medical community, especially for gender-diverse individuals where their perceived gender conflates with their assigned grouping causing patient-specific distress. The distinction between gender-a multifactorial perception of identity-and biological sex is addressed under new iPLEDGE guidelines. Dermatologists now register patients under one of 2 categories: patients who can become pregnant and those who cannot become pregnant. This change simultaneously improves the accessibility to isotretinoin among gender-diverse individuals, while limiting prescription barriers. Despite initial success being limited due to lengthy system conversions, a registration process based on reproductive potential ultimately enhances iPLEDGE's goal to prevent potential birth defects. We propose that other REMS programs follow the standard set by the iPLEDGE system, including those for the medications thalidomide, acitretin, and mycophenolate mofetil, all of which currently have a similar taxonomy to that of the old iPLEDGE system. Implementing the standardization of gender-neutral terminology can maximize enrollment and minimize distress. Current and ongoing refinement of iPLEDGE and other REMS is needed to build protocols solely around the prevention of birth defects without regard to sex or gender.
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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 | 0.230 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.029 | 0.049 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.045 | 0.071 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".