ADHD is associated with an increased risk of written-language disorder
Bibliographic record
Abstract
### Question What is the incidence of written-language disorder (WLD), with and without reading disability (RD), among children with and without attention-deficit/hyperactivity disorder (ADHD)? ### Population 5699 children born between 1st January 1976 and 31st December 1982 in one school district, who still lived in the area after age 5 years, and who did not have clinical diagnoses of severe intellectual disability or full-scale IQ scores <50. ### Setting Rochester, Minnesota, USA; participants born from 1976 to 1982. ### Assessment Children were assessed for ADHD, WLD and RD retrospectively from birth until death, emigration or graduation using school and medical records. Individuals with ADHD had to meet DSM-IV criteria of ADHD; have positive ADHD questionnaire results; or have a clinical diagnosis of ADHD. The date of ADHD diagnosis was either when clinical diagnosis was made, or in its absence the date of the first positive ADHD specific questionnaire or the date when DSM-IV research criteria were met. WLD and RD were diagnosed by comparing writing or reading achievement scores with cognitive ability test results. WLD or RD were diagnosed if the standard score at any time point was >1.75 SD below the standard score predicted by an IQ test; the difference between the achievement scores and IQ scores was …
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".