Patterns of relapse and associated cost burden in schizophrenia patients receiving atypical antipsychotics
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Bibliographic record
Abstract
OBJECTIVE: To identify relapse in schizophrenia and the main cost drivers of relapse using a cost-based algorithm. METHODS: Multi-state Medicaid data (1997-2010) were used to identify adults with schizophrenia receiving atypical antipsychotics (AP). The first schizophrenia diagnosis following AP initiation was defined as the index date. Relapse episodes were identified based on (1) weeks during the ≥2 years post-index associated with high cost increase from baseline (12 months before the index date) and (2) high absolute weekly cost. A compound score was then calculated based on these two metrics, where the 54% of patients associated with higher cost increase from baseline and higher absolute weekly cost were considered relapsers. Resource use and costs of relapsers during baseline and relapse episodes were compared using incidence rate ratios (IRRs) and bootstrap methods. RESULTS: In total, 9793 relapsers were identified with a mean of nine relapse episodes per patient. Duration of relapse episodes decreased over time (mean [median]; first episode: 34 [4] weeks; remaining episodes: 8 [1] weeks). Compared with baseline, resource utilization during relapse episodes was significantly greater in pharmacy, outpatient, and institutional visits (hospitalizations, emergency department visits), with IRRs ranging from 1.9-2.4 (all p < 0.0001). Correspondingly, relapse was associated with a mean (95% CI) incremental cost increase of $2459 ($2384-$2539) per week, with institutional visits representing 53% of the increase. LIMITATIONS: Relapsers and relapse episodes were identified using a cost-based algorithm, as opposed to a more clinical definition of relapse. In addition, their identification was based on the assumption from literature that ~54% of schizophrenia patients will experience at least one relapse episode over a 2-year period. CONCLUSIONS: Significant cost increases were observed with relapse in schizophrenia, driven mainly by institutional visits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it