Secular Trend of Sex Ratio and Symptom Patterns among Children with Autism Spectrum Disorders
Bibliographic record
Abstract
An information technology invention embodied in a website serving the interests of the autism community was designed to "let the data talk." By its use, the authors have detected a downward temporal trend in 2013 in the sex ratio of 2431 members of Autism360.org from a yearly average between 2010 and 2012 of 4.24 to 3.01 in 2013. As of the first two months of 2014, the average sex ratio is 2.69. We report contemporaneous changes in previously reported male vs female symptom patterns. Such changes suggest a convergence in which distinctive severity of certain grouped central nervous, emotional, and immune profile items in females have diminished toward that of males. The data also show correlations among these profile items that add further credence to the sex ratio findings. A wider dispersion of the female data as compared with the male data was found in the year preceding the downward trend in the mean sex ratio. The authors suggest that such a trend toward an increase in the variance of the data points to instability in the biological system-the autism spectrum. We conclude that public policy would be better served by monitoring changes in the standard deviation as compared with the mean in large data sets to better anticipate changes. The findings we report raise questions based on known sex differences in detoxification chemistry. One such question would be whether maternal, fetal, or individual exposure to a novel environmental factor may have breached the taller fence of female protection from toxins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".