Is Monotherapy as Good as Polypharmacy in Long—Term Treatment of Bipolar Disorder?
Bibliographic record
Abstract
Can J Psychiatry. 2009;54(1 1):719-725. Can Trial and Error Become the Standard of Practice? During the last several decades, the number of available treatments for bipolar disorder (BD) has increased substantially. Nevertheless, the outcome of BD remains poor in many patients,1 fuelling debates about the merits of specific medications and their combinations. Treatment of BD has several facets - treatment of acute episodes of depression or mania, long-term treatment, and control of rapid cycling, cognitive dysfunction, or residual symptoms. Not all can be addressed in a brief paper. In the following, I will focus on long-term treatment, mainly as this is the key component in the disease management. Treatment of acute episodes is, perhaps, less controversial, particularly when it comes to manic episodes - using a combination treatment may shorten time to remission and, importantly, such treatments are typically used for a limited period of time. But even then some add-on trials in mania have produced equivocal data. The situation concerning bipolar depression is far more complicated, with many studies, including newer data, not supporting the use of combinations with antidepressants.2 Therefore, even when treating patients for acute episodes, physicians must carefully judge the risk-benefit balance (with risk of switch into mania, rapid cycling, or adverse effects). Are There Reasons to Use Combinations for Long-Term Treatment? A large proportion of patients with BD are being treated off label, and many are treated with combinations of not only 2, but frequently 3 or more medications aimed at mood stabilization. The evidence to support such management is practically nonexistent, as shown below. Before reviewing the evidence, I would like to stress 2 other points. First, physicians need to be able to determine accurately that a patient is better off because of a specific treatment and whether an alternative treatment might provide the same or greater benefit. This is not easy in an illness that is highly unpredictable in its pattern of recurrences (and spontaneous remissions). Many patients are treated for limited periods of time, making evaluation of treatment response difficult. Second, with more research we may eventually know if, on average, a randomly selected combination presents an advantage over randomly selected monotherapy. However, this is not the same as knowing if a combination is better than individualized treatment. There is a growing trend to find predictors of treatment response, including clinical features, biological markers, as well as genetic variants. Evidence-based selection of an effective treatment may make this present debate ultimately less relevant than it currently seems. For some drugs, for instance lithium, the long-term response can be already predicted quite accurately using clinical data alone.3 The limiting factor in deciding whether polypharmacy presents an advantage over monotherapy is the lack of relevant studies. Available data are not helpful to answer such simple questions as to whether one should initiate long-term treatment with drug combinations, or whether an ineffective medication should be switched or combined (augmented). Existing studies of combination treatments are either inconclusive, methodologically problematic, or answer another question altogether. A common reason for using drug combinations is monotherapy failure. No existing medication works for all cases of BD. After concluding that a person is not responding, a decision needs to be made about the future direction of the treatment. This may, but does not have to, be a medication change. Treatment outcome can be influenced by therapeutic alliance, improving compliance and providing patient education, as well as addressing side effects. In some cases, reassessment may show that the patient was initially misdiagnosed and requires a different treatment altogether. Data on effectiveness of combinations are scarce. …
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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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".