Autism BrainNet: A Collaboration Between Medical Examiners, Pathologists, Researchers, and Families to Advance the Understanding and Treatment of Autism Spectrum Disorder
Bibliographic record
Abstract
CONTEXT.—: Autism spectrum disorder is a neurodevelopmental condition that affects over 1% of the population worldwide. Developing effective preventions and treatments for autism will depend on understanding the neuropathology of the disorder. While evidence from magnetic resonance imaging indicates altered development of the autistic brain, it lacks the resolution needed to identify the cellular and molecular underpinnings of the disorder. Postmortem studies of human brain tissue currently represent the only viable option to pursuing these critical studies. Historically, the availability of autism brain tissue has been extremely limited. OBJECTIVE.—: To overcome this limitation, Autism BrainNet, funded by the Simons Foundation, was formed as a network of brain collection sites that work in a coordinated fashion to develop a library of human postmortem brain tissues for distribution to researchers worldwide. Autism BrainNet has collection sites (or Nodes) in California, Texas, and Massachusetts; affiliated, international Nodes are located in Oxford, England and Montreal, Quebec, Canada. DATA SOURCES.—: Pubmed, Autism BrainNet. CONCLUSIONS.—: Because the death of autistic individuals is often because of an accident, drowning, suicide, or sudden unexpected death in epilepsy, they often are seen in a medical examiner's or coroner's office. Yet, autism is rarely considered when evaluating the cause of death. Advances in our understanding of chronic traumatic encephalopathy have occurred because medical examiners and neuropathologists questioned whether a pathologic change might exist in individuals who played contact sports and later developed severe behavioral problems. This article highlights the potential for equally significant breakthroughs in autism arising from the proactive efforts of medical examiners, pathologists, and coroners in partnership with Autism BrainNet.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.026 |
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".