Drug, Patient, and Physician Characteristics Associated With Off-label Prescribing in Primary Care
Bibliographic record
Abstract
BACKGROUND: Off-label prescribing may lead to adverse drug events. Little is known about its prevalence and determinants resulting from challenges in documenting treatment indication. METHODS: We used the Medical Office of the XXI Century electronic health record network in Quebec, Canada, where documentation of treatment indication is mandatory. One hundred thirteen primary care physicians wrote 253 347 electronic prescriptions for 50 823 patients from January 2005 through December 2009. Each drug indication was classified as on-label or off-label according to the Health Canada drug database. We identified off-label uses lacking strong scientific evidence. Alternating logistic regression was used to estimate the association between off-label use and drug, patient, and physician characteristics. RESULTS: The prevalence of off-label use was 11.0%; of the off-label prescriptions, 79.0% lacked strong scientific evidence. Off-label use was highest for central nervous system drugs (26.3%), including anticonvulsants (66.6%), antipsychotics (43.8%), and antidepressants (33.4%). Drugs with 3 or 4 approved indications were associated with less off-label use compared with drugs with 1 or 2 approved indications (6.7% vs 15.7%; adjusted odds ratio [AOR], 0.44; 95% CI, 0.41-0.48). Drugs approved after 1995 were prescribed off-label less often than were drugs approved before 1981 (8.0% vs 17.0%; AOR, 0.46; 95% CI, 0.42-0.50). Patients with a Charlson Comorbidity Index of 1 or higher had lower off-label use than did patients with an index of 0 (9.6% vs 11.7%; AOR, 0.94; 95% CI, 0.91-0.97). Physicians with evidence-based orientation were less likely to prescribe off-label (AOR, 0.93; 95% CI, 0.88-0.99), a 7% reduction per 5 points in the evidence section of the Evidence-Practicality-Conformity Scale. CONCLUSIONS: Off-label prescribing is common and varies by drug, patient, and physician characteristics. Electronic prescribing should document treatment indication to monitor off-label use.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".