Commentary: sex difference differences? A reply to Constantino
Bibliographic record
Abstract
Messinger et al. found a 3.18 odds ratio of male to female ASD recurrence in 1241 prospectively followed high-risk (HR) siblings. Among high-risk siblings (with and without ASD), as well as among 583 low-risk controls, girls exhibited higher performance on the Mullen Scales of Early Learning, as well as lower restricted and repetitive behavior severity scores on the Autism Diagnostic Observation Schedule (ADOS) than boys. That is, female-favoring sex differences in developmental performance and autism traits were evident among low-risk and non-ASD high-risk children, as well as those with ASD. Constantino (Mol Autism) suggests that sex differences in categorical ASD outcomes in Messinger et al. should be understood as a female protective effect. We are receptive to Constantino's (Mol Autism) suggestion, and propose that quantitative sex differences in autism-related features are keys to understanding this female protective effect.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.046 | 0.050 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".