Time to Rethink the Default Settings in Neuroscience: Hormonal Transition Periods as Natural Experiments and Why Sex Matters.
Bibliographic record
Abstract
Diversity drives scientific discovery. Yet, many basic and clinical neuroscience studies fail to include equal numbers of females in their samples, and even fewer present sex-specific analysis of their data. We propose the following strategies to overcome this bias: (1) increase numbers of female study participants, (2) consider sex as a primary variable, and (3) when justified, study all-female samples to provide a more indepth understanding of female-specific experiences such as the menstrual cycle as well as sex-specific risk trajectories and pathologies. In our research program, we study the influence of sex and sex hormones on brain states in health and disease. We strive to explain the mechanisms underlying the unique vulnerability of women to depression and dementia. Our ultimate goal is to improve brain health for both sexes. By applying scientific expertise in neuropharmacology, quantitative neurochemical imaging and sex differences to traditional research questions in the cognitive sciences, we provide novel perspectives on the diversity of human cognition and brain plasticity. Knowledge gaps and missing data are often the primary driving force behind the development of scientific breakthroughs. The curiosity and ur-
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".