Long-Acting Injectable Antipsychotics in a Prescription Claims Data Source: A Validation Study
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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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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".