Editors’ Note and Prologue
Bibliographic record
Abstract
* Abbreviations: AIR-P — : Autism Intervention Research Network on Physical Health ASD — : autism spectrum disorder ATN — : Autism Treatment Network ED — : emergency department HRSA — : Health Resources and Services Administration MCHB — : Maternal and Child Health Bureau Children with autism spectrum disorder (ASD) experience significant developmental challenges and complex co-occurring medical and psychiatric conditions,1–3 and they are at risk for considerable unmet health care needs.4–8 As the reported prevalence of ASD continues to rise,9,10 the need for a health care system that is fully equipped to meet the needs of this population is increasingly important. We are pleased to introduce the third supplement to Pediatrics led by the Autism Intervention Research Network on Physical Health (AIR-P) with support from the Maternal and Child Health Bureau (MCHB) of the Health Resources and Services Administration (HRSA) through the Autism Collaboration, Accountability, Research, Education, and Support Act.11 The 2 previous supplements led by the AIR-P were published in 2012 and 2016, and the work presented in this volume represents significant advances in improving the health and well-being of children with ASD through innovations in research and practice. The articles in this supplement describe infrastructure investments, multiinstitutional collaborations, new models of practice, and original research focused on improving care in multiple locations and contexts for children with ASD and their families. The work reflected in this supplement would not have been possible without considerable federal and private foundation funding. This infrastructure support enabled researchers, clinicians, family members, and key stakeholders to engage in dynamic partnerships to share knowledge and improve systems of care. The supplement begins with an overview of the reach of the HRSA-MCHB Autism and Other Developmental Disabilities Program and its alignment with federal priorities for ASD research.11 The AIR-P represents 1 of 5 autism research networks funded by HRSA and MCHB. In 2008, the … Address correspondence to Evdokia Anagnostou, MD, Holland Bloorview Kids Rehabilitation Hospital, 150 Kilgour Road, Toronto, ON M4G1R8, Canada. Email: eanagnostou{at}hollandbloorview.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.068 | 0.045 |
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".