About 3 in every 1000 US children in large metropolitan areas may have autism or related developmental disorders
Bibliographic record
Abstract
Yeargin-Allsopp M, Rice C, Karapurkar T et al. Prevalence of autism in a US metropolitan area. JAMA2003 Jan; 289 : 49 –55 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: What is the prevalence of autism among children in Atlanta, Georgia? Database and documentary analysis. Metropolitan Atlanta, Georgia, USA; 1996. The records of all children aged 3–10 years in Atlanta in 1996 were screened (n=289,456; 51% male; 58% white, 38% black). Prevalence of autism stratified by demographic factors, cognitive functioning, previous autism diagnoses, and source of information. Multiple medical and educational records were screened to identify children with autism. Case status was determined by expert review. 987 children displayed behaviours consistent with autistic disorder, pervasive developmental disorder - not otherwise specified, or Asperger disorder according to the Diagnostic and Statistical Manual of Mental Disorders (4th edition). The prevalence of autism … [1]: {openurl}?query=rft.jtitle%253DJAMA%26rft.stitle%253DJAMA%26rft.aulast%253DYeargin-Allsopp%26rft.auinit1%253DM.%26rft.volume%253D289%26rft.issue%253D1%26rft.spage%253D49%26rft.epage%253D55%26rft.atitle%253DPrevalence%2Bof%2BAutism%2Bin%2Ba%2BUS%2BMetropolitan%2BArea%26rft_id%253Dinfo%253Adoi%252F10.1001%252Fjama.289.1.49%26rft_id%253Dinfo%253Apmid%252F12503976%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/jama.289.1.49&link_type=DOI [3]: /lookup/external-ref?access_num=12503976&link_type=MED&atom=%2Febmental%2F6%2F3%2F73.atom [4]: /lookup/external-ref?access_num=000180136600025&link_type=ISI
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".