Greater than the Sum: Applying Daily-Dose Equivalents to Antipsychotic Prescription Claims to Study Real-World Effects
Bibliographic record
Abstract
Traditional methods to standardize exposures in pharmacoepidemiologic studies, like defined daily-doses, may be inadequate to capture drug class effects when there are many in-class medications, formulations, and administration routes. Antipsychotic medications are one example of a drug class with these complexities. Direct dose conversion methods are pharmacologically-based but often overlooked, potentially for lack of real-world guidance and examples of their implementation. The purpose of this article is to describe a method to implement dose conversion, using an example study that quantifies antipsychotic use among a cohort of older adults with dementia. We identified 45,442 older adults (aged ≥66 years) with dementia initiating antipsychotic therapy between January 1, 2009 and December 31, 2012 in Ontario, Canada using linked administrative healthcare databases. We developed and applied a data cleaning and dose conversion algorithm to quantify antipsychotic exposure in chlorpromazine dose equivalents at initiation, month 6, and month 12 of therapy. Results were stratified by route of administration. At initiation, 14% of patients received multiple antipsychotic prescriptions simultaneously. Patients initiating regular injectable and multiple administration routes received the highest median chlorpromazine equivalent daily-doses. Data cleaning changed 3, 16, 36, and 42% of total equivalent daily-doses in patients initiating oral, regular injectable, long-acting injectable, and multiple administration routes, respectively. Dose conversion of prescription claims data was a feasible method to quantify and present antipsychotic drug exposures. Dose conversion methods can be considered for drug effects studies of antipsychotic therapies and other medication classes with complex use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".