Economic Evaluation of Interventions for Children with Neurodevelopmental Disorders: Opportunities and Challenges
Bibliographic record
Abstract
Economic evaluation is a tool used to inform decision makers on the efficiency of comparative healthcare interventions and inform resource allocation decisions. There is a growing need for the use of economic evaluations to assess the value of interventions for children with neurodevelopmental disorders (NDDs), a population that has increasing demands for healthcare services. Unfortunately, few evaluations have been conducted to date, perhaps stemming from challenges in applying existing economic evaluation methodologies in this heterogeneous population. Opportunities exist to innovate methods to address key challenges in conducting economic evaluations of interventions for children with NDDs. In this paper, we discuss important considerations and highlight areas for future work. This includes the paucity of appropriate instruments for measuring outcomes meaningful to children with NDDs and their families, difficulties in the measurement of costs due to service utilization in a wide variety of sectors, complexities in the measurement of caregiver and family effects and considerations in estimating long-term productivity costs. Innovation and application of evaluation approaches in these areas will help inform decisions around whether the resources currently spent on interventions for children with NDDs represent good value for money, or whether greater benefits for children could be generated by spending money in other ways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".