Autism screening and diagnosis in low resource settings: Challenges and opportunities to enhance research and services worldwide
Bibliographic record
Abstract
Most research into the epidemiology, etiology, clinical manifestations, diagnosis and treatment of autism is based on studies in high income countries. Moreover, within high income countries, individuals of high socioeconomic status are disproportionately represented among participants in autism research. Corresponding disparities in access to autism screening, diagnosis, and treatment exist globally. One of the barriers perpetuating this imbalance is the high cost of proprietary tools for diagnosing autism and for delivering evidence-based therapies. Another barrier is the high cost of training of professionals and para-professionals to use the tools. Open-source and open access models provide a way to facilitate global collaboration and training. Using these models and technologies, the autism scientific community and clinicians worldwide should be able to work more effectively and efficiently than they have to date to address the global imbalance in autism knowledge and at the same time advance our understanding of autism and our ability to deliver cost-effective services to everyone in need.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".