Switching from Brand‐Name to Generic Psychotropic Medications: A Literature Review
Bibliographic record
Abstract
Generic medications do not undergo the rigorous approval process required of original medications. Their effectiveness and safety is expected to be equal to that of their more expensive counterparts. However, several case reports and studies describe clinical deterioration and decreased tolerability with generic substitution. Pubmed was searched from January 1, 1974 to March 1, 2010. The MeSH term "generic, drugs" was combined with "anticonvulsants," "mood stabilizers," "lithium," "antidepressants," "antipsychotics," "anxiolytics," and "benzodiazepines." Additional articles were obtained by searching the bibliographies of relevant references. Articles in English, French, or Spanish were considered if they discussed clinical equivalence of generic and brand-name medications, generic substitution, or issues about effectiveness, tolerability, compliance, or economics encountered with generics. Clinical deterioration, adverse effects, and changes in pharmacokinetics are described with generic substitution of several anticonvulsants/mood stabilizers (carbamazepine, valproate, lamotrigine, gabapentin, topiramate, lithium), antidepressants (amitriptyline, nortriptyline, desipramine, fluoxetine, paroxetine, citalopram, sertraline, venlafaxine, mirtazapine, bupropion), antipsychotics (risperidone, clozapine), and anxiolytics (clonazepam, alprazolam). Generics do not always lead to the anticipated monetary savings and also raise compliance issues. Although the review is limited by publication bias and heterogeneity of the studies in the literature, we believe there is enough concern to advise generic switching on an individual basis with close monitoring throughout the transition. Health professionals should be aware of the stakes around generic substitution especially when health economics promote universal use of generics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".