Long-Acting Injectable Antipsychotics in a Prescription Claims Data Source: A Validation Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The effectiveness of long-acting injectable antipsychotics (LAIAs) has been demonstrated in studies using prescription claims data. However, the validity of claims data for LAIAs has not been established. OBJECTIVE: We aimed to validate date dispensed, quantity dispensed and days supplied fields in prescription claims data, and to compare claims- and medical record-derived persistence estimates. METHODS: We evaluated LAIA dispensations in the Drug Programs Information Network prescription claims database from Manitoba, Canada against a random sample of medical records. Adults with one or more LAIA prescription between April 2015 and March 2016 were eligible. Results were stratified by LAIA type (first-generation LAIA, risperidone LAI or paliperidone LAI). Persistence estimates were assessed using Kaplan-Meier survival analysis and proportion of patients covered method. RESULTS: Claims data had high positive predictive value, ranging from 80.0% (95% CI 51.9-95.7) to 100.0% (95% CI 89.7-100.0), but low negative predictive value, ranging from 0.0% (95% CI 0.0-2.5) to 62.5% (95% CI 40.6-81.2). Quantity dispensed and days supplied exactly matched dose and dosing interval, respectively, for 99.7% and 97.1% of risperidone LAI doses, 100.0% and 76.6% of paliperidone doses, and 8.9% and 28.3% of first-generation LAIA doses. There were no significant differences in claims-derived versus medical record-derived persistence estimates. CONCLUSIONS: Quantity dispensed and days supplied provide valid estimates of dose and dosing interval for second-generation LAIAs, but underestimated these parameters for first-generation LAIAs. However, a large proportion of medical record-confirmed doses were missing from claims data, and dose and dosing interval are underestimated in claims data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it