Why it is so challenging to perform economic evaluations of interventions in autism and what to do about it
Bibliographic record
Abstract
Economic evaluation is used to determine the optimal provision of services and programs under budget constraints and to inform public and private payer funding decisions. To maximize value-for-money in the design and delivery of programs and services for persons with autism spectrum disorder (ASD), it's essential to generate high-quality economic evidence to inform budget allocation. There is a paucity however, of economic evaluations of interventions for ASD. This is due in part to challenges in conducting economic evaluations in this population and the lack of guidance on suitable approaches. These challenges are related to the inherent heterogeneity of the autistic population; establishing short- and long-term effectiveness; measurement of costs and the availability of valid instruments for collecting economic data; the appropriateness of outcomes for use in economic evaluation; and achieving statistical power. This commentary addresses a lack of awareness and needed guidance on these issues by discussing the challenges and providing recommendations for how economic evaluations in ASD could be improved to generate high-quality evidence for program funding decision-making.
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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 | 0.116 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.016 | 0.015 |
| 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".