Obesity and mixed features in bipolar disorder: A systematic review
Bibliographic record
Abstract
There is some clinical evidence indicating that obesity may change the trajectory of individuals with Bipolar Disorder (BD) to having more mixed features, however, to the best of our knowledge, there are no systematic reviews investigating this relationship. Thus, this systematic review is aimed at describing the association between obesity and mixed features in BD, specifically whether obesity changes the trajectory of patients with BD to having a greater frequency of mixed features. A literature search was conducted on October 22, 2021, using the following databases: PubMed, PsycINFO, and EMBASE. A total of 2 studies were included in this systematic review. Both studies described the frequency of mixed features in a population of patients with BD with and without the presence of obesity. Among individuals with BD, patients who were obese had a significantly greater probability of having mixed features than patients who were not obese. The prevalence of manic versus depressive or mixed mood states was significantly associated with obesity, since a higher percentage of patients who had BD and were obese presented with depressive or mixed mood states compared to patients who had BD and were not obese. Patients that are diagnosed with BD and present with obesity have a greater prevalence of presenting mixed features, which has been linked to worse clinical outcomes. More studies are needed to definitively determine the association between obesity and mixed features, and therefore, implement interventions to improve prognosis in patients with BD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".