Cost-Effectiveness of Comprehensive, Integrated Care for First Episode Psychosis in the NIMH RAISE Early Treatment Program
Bibliographic record
Abstract
This study compares the cost-effectiveness of Navigate (NAV), a comprehensive, multidisciplinary, team-based treatment approach for first episode psychosis (FEP) and usual Community Care (CC) in a cluster randomization trial. Patients at 34 community treatment clinics were randomly assigned to either NAV (N = 223) or CC (N = 181) for 2 years. Effectiveness was measured as a one standard deviation change on the Quality of Life Scale (QLS-SD). Incremental cost effectiveness ratios were evaluated with bootstrap distributions. The Net Health Benefits Approach was used to evaluate the probability that the value of NAV benefits exceeded its costs relative to CC from the perspective of the health care system. The NAV group improved significantly more on the QLS and had higher outpatient mental health and antipsychotic medication costs. The incremental cost-effectiveness ratio was $12 081/QLS-SD, with a .94 probability that NAV was more cost-effective than CC at $40 000/QLS-SD. When converted to monetized Quality Adjusted Life Years, NAV benefits exceeded costs, especially at future generic drug prices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".