Revisión de la discusión en torno a la incorporación del sexo como variable biológica (SABV) en investigación preclínica
Bibliographic record
Abstract
The examination of sex as a biological variable (SABV) in preclinical research presents unique challenges and has gained significant prominence over the past decade. This article critically examines the ongoing debates surrounding the incorporation of SABV, emphasizing the limitations of the current binary framework that restricts the understanding of sex to a simplistic, a priori classification of male and female. Such an approach perpetuates an essentialist perspective of sexual difference, potentially resulting in biased research outcomes and inadequate medical treatments. We advocate for a contextualist perspective, aligning with Richardson's (2022) concept of “sex contextualism,” which interprets sex as a set of variable biological characteristics across multiple biological levels and experimental contexts. We argue that this approach enhances the precision and relevance of scientific findings, and provides a more accurate representation of the biological and social diversity of the human population. This paper provides specific examples and advocates for a more inclusive and equitable methodology in integrating sex into preclinical research.
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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 | 0.044 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".