The Role Of Sales Representatives In Driving Physicians’ Off-Label Prescription Habits
Bibliographic record
Abstract
Off-label prescribing is widespread in Canada and the United States (U.S.). One in nine prescriptions for Canadian adults are for off-label uses with the highest percentages coming from anticonvulsants (66.6 percent), antipsychotics (43.8 percent), and antidepressants (33.4 percent). Overall, 79 percent of the off-label prescriptions lacked strong scientific evidence for their use. For 160 drugs commonly prescribed to U.S. adults and children, 21 percent were for off-label indications totaling 150 million prescriptions. In this case, 73 percent had little to no scientific backing and once again psychoactive drugs such as gabapentin had the highest level of off-label use. Moreover, doctors do not seem to know what are and are not approved FDA use for many of the drugs that they prescribe. Now an article published in the June issue of Health Affairs by Ian Larkin and colleagues points to active promotion by sales representatives as one reason for the widespread off-label use of antipsychotics and antidepressants in children.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".