Off-Label Use of Second Generation Antipsychotics in Primary Care -An Exploratory Study
Bibliographic record
Abstract
Over the past two decades, the use of antipsychotics has increased tremendously worldwide, and second-generation antipsychotics (SGAs) have been the main driver of this trend. The extensive use of SGAs for off-label purposes has raised concerns over their role in clinical practice. In particular, studies have revealed serious metabolic and cardiovascular effects, and evidence is lacking on SGAs’ effectiveness. Despite the concerns, the extent and pattern of SGAs’ off-label use is largely unknown within the context of the Canadian primary health care system. Using electronic medical record (EMR) data from 14 practices in southwestern Ontario, we investigated the number of patients who were prescribed SGAs in primary care for off-label uses between 2005 and 2015. Furthermore, we compared the history of diagnosis of the off-label population to this history of a reference population (non-SGA users) in the same setting.\nThe majority of patients who were prescribed SGAs lacked records of approved indications (72%), and the medications appeared to be prescribed much more frequently for off- than on-label uses in any given year in the study period. SGAs are reported to be prescribed off-label for a variety of conditions; in our data, SGA users in the off-label group were more likely to have a history of dementia, anxiety and depressive disorders, personality disorders, and substance abuse, which may have been the off-label indications for which the patients were prescribed SGAs in primary care.\nOur findings indicate a need to promote evidence-based prescription of SGAs as well as the provision of further evidence on their use in off-label indications. Although off-label use has often preceded and outstripped supporting evidence, we encourage the regulatory agency, pharmaceutical industry, and science community to implement innovative policies and solutions to address the off-label prescribing practice
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".