Bibliographic record
Abstract
Neuroscience today relies on the overwhelming belief that biological sex does not matter and can be safely ignored in preclinical research. Common practice within neuroscientific research is that findings in one sex (usually males) can be generalized to the other sex (usually females). Authors will even take the extreme approach of developing questionable methods to “prove” that sex differences are not present in the brain. Sex matters not only at the macroscopic level, where male and female brains have been found to differ in size and connectivity, but at the microscopic level too. This themed issue of the Journal of Neuroscience Research highlights sex differences of the brain at all scales, from the genetic and epigenetic, to the synaptic, cellular, and systems differences—differences known to be present throughout the life span. The work published in this issue powerfully illustrates that sex matters and that researchers can no longer rely on extrapolation from research on male animals and cells, which obscures key differences that might influence clinical studies. Consistent with NIH policy towards grant applications as of February 2016 (see http://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-102.html), the lack of an existing literature concerning the likelihood of a sex influence in a given domain does not constitute an adequate rationale for failing to examine a dataset for potential sex differences. Rather, testing for sex as a biological variable will give us the power to both transform our understanding of female and male biology and pathophysiology and, most importantly, inform clinical research. It is an issue whose time has come. Eric M Prager, PhD Editor-in-Chief, Journal of Neuroscience Research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".