Month of Conception and Learning Disabilities: A Record-Linkage Study of 801,592 Children
Bibliographic record
Abstract
Learning disabilities have profound, long-lasting health sequelae. Affected children born over the course of 1 year in the United States of America generated an estimated lifetime cost of $51.2 billion. Results from some studies have suggested that autistic spectrum disorder may vary by season of birth, but there have been few studies in which investigators examined whether this is also true of other causes of learning disabilities. We undertook Scotland-wide record linkage of education (annual pupil census) and maternity (Scottish Morbidity Record 02) databases for 801,592 singleton children attending Scottish schools in 2006-2011. We modeled monthly rates using principal sine and cosine transformations of the month number and demonstrated cyclicity in the percentage of children with special educational needs. Rates were highest among children conceived in the first quarter of the year (January-March) and lowest among those conceived in the third (July-September) (8.9% vs 7.6%; P < 0.001). Seasonal variations were specific to autistic spectrum disorder, intellectual disabilities, and learning difficulties (e.g., dyslexia) and were absent for sensory or motor/physical impairments and mental, physical, or communication problems. Seasonality accounted for 11.4% (95% confidence interval: 9.0, 13.7) of all cases. Some biologically plausible causes of this variation, such as infection and maternal vitamin D levels, are potentially amendable to intervention.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".