The Role of Prescribing Generic (Non-proprietary) Drugs in the Prevalence of Therapeutic Inertia in Multiple Sclerosis Care
Bibliographic record
Abstract
Importance: Generics companies may market products at low or similar costs to brand drug competitors given the more flexible compliance rules, and regulations for marketing their products in the health system. This phenomenon may influence prescriber’s behavior. Objectives: To compare the prevalence of therapeutic inertia (TI) between primary prescription of brand-name vs. generic drugs in the management of MS in Argentina. Design: Population-based online study comprising 117 neurologists with expertise in MS. Participants answered questions regarding their clinical practice, most commonly prescribed disease modifying agents, and therapeutic choices of 10 simulated case-scenarios that assessed TI. Inertia was defined as the lack of treatment initiation or escalation despite evidence of clinical and radiological activity (8 case-scenarios, 720 individual responses). We created the generic-brand name score (GBS) according to the 5 most frequently prescribed generic (n=16) vs. brand-name (n=9) drugs for MS, where scores higher than 1 indicated higher prescription of generic drugs and scores lower than 1 indicated higher prescription of brand agents. The primary outcome was TI. Results: 90 participants completed the study (completion rate 76.9%). TI was observed in 153 (21.3%) of participants’ responses. The mean GBS score (SD) of 3.44 (2.1), with 46 (51.1%) participants having a GBS equal to or higher than 1. Older age (OR 1.19; 95%CI 1.00-1.42), being a general neurologist (OR 3.91; 95%CI 1.19-12.8) and being more willing to take risks in multiple domains (SOEP score OR 1.06, 95%CI 1.01-1.12) were associated with higher prescription of generic drugs in MS care. The evaluation of individual responses revealed that participants with higher prescription of generics – reflected by a higher GBS – had higher incident risk of TI (mean GBS 3.61 for TI vs. 2.96 for no TI; p<0.001). Multivariate analysis revealed that a prescription of generic agents was associated with an increased incident risk of TI (OR 1.56; 95%CI 1.07-2.29). Conclusions: General neurologist, older age, and willingness to take risks were associated with increased prescription of generic drugs despite similar costs compared to brand-agents. In our study, the prescription of generic-MS drugs was associated with a higher incident risk of TI.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".