When are sex-specific effects really sex-specific?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examined developmental programming studies that reported sex-specific effects published between 2012 and 2014, and examined whether the authors reported a statistical approach to explicitly test whether the effect of treatment differed between the sexes, for example, a sex by treatment interaction term. Less than half of the studies that reported sex-specific effects described explicitly testing whether effects were indeed sex-specific; in most cases, an effect was considered 'sex-specific' if it was significant in one sex but not the other. This is not a robust approach, since significance in one sex and lack of significance in the other sex does not imply a significant difference between the sexes. However, sample size often limits statistical power to detect interactions. We suggest that if the effect is significant in only one sex, but the interaction term is not significant, alternative solutions would be to present the confidence intervals for the effect size for each sex, or using Bayesian approaches to calculate the probability that the effect sizes differ between the sexes. We present a simple example of a Bayesian analysis to illustrate that this approach is reasonably easy to implement and interpret.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it