Utilization of herbal and nutritional compounds among older adults with bipolar disorder and with major depression
Bibliographic record
Abstract
OBJECTIVES: Herbal and nutritional compounds (HNC) are widely used among geriatric populations with depression, however little data exists on HNC use in older populations with bipolar disorder. The goal of this study was to evaluate orally- ingested HNC use in individuals with bipolar disorder and with major depression. METHODS: This was a cross-sectional analysis of self-reported factual knowledge of HNC, individual perspective on efficacy and safety of HNC, patterns of HNC use, and discussion of HNC with health care providers in 50 older adults with bipolar disorder and 50 older adults with major depression. RESULTS: In this sample, approximately 30% of older individuals with depression or bipolar disorder used orally- ingested HNC. Over 40% of older adults believed that HNC is FDA-regulated and 14-20% preferred to take HNC compared to physician-prescribed psychotropic medications. Use of HNC was more common among older adults with bipolar disorder (44%) compared to older adults with major depression (16%, p = 0.003). The majority of older adults with mood disorders (64%) had not discussed use of HNC with their treating physicians. CONCLUSION: Orally ingested HNC was used by nearly one in three older adults with mood disorders, and was more common among those with bipolar disorder compared to those with major depression. Most individuals did not discuss HNC use with their physicians. Clinicians need to assess for HNC use, particularly with respect to potential drug-drug interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".