Results From an Online Survey of Patient and Caregiver Perspectives on Unmet Needs in the Treatment of Bipolar Disorder
Bibliographic record
Abstract
OBJECTIVE: To look at the manner in which patients and caregivers perceive the treatment of bipolar disorder compared with the evidence base for bipolar treatment. METHOD: Between April 2013 and March 2014, 469 respondents took a 14-question online survey on demographics, medications taken, and perspectives on bipolar treatment and medications. Participants were recruited through social media outlets (Facebook and Twitter accounts) of Global Medical Education (New York, New York) and the blog Bipolar Burble, which has a primary audience of people with bipolar disorder. There were no exclusion criteria to participation, and both patients and health care professionals were encouraged to participate. RESULTS: Most respondents were taking ≥ 3 medications, and the greatest unmet need in treatment was for bipolar depression. In general, respondent perspectives on the effectiveness of individual medication treatments did not align with the available literature. Weight gain was the greatest side effect concern for both antipsychotics and mood stabilizers. CONCLUSIONS: Our survey demonstrates that there are still many unmet needs in the treatment of bipolar disorder. There is also a mismatch between the evidence base for treatments in bipolar disorder and patient perception of the relative efficacy of different medications. In order to achieve better outcomes, there is a need to provide patients and clinicians greater quality education with regard to the best evidence-based treatments for bipolar disorder.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".