Wait-there’s evidence for that? Integrative medicine treatments for major depressive disorder
Bibliographic record
Abstract
Depression is one of the most common mental health disorders and currently affects over 17 million Americans. Up to two-thirds of patients with depression in the United States will seek complementary and alternative or integrative medical treatments and thus medical providers who treat depression should understand that many integrative medical treatments have evidence of efficacy either as monotherapies or as add-on adjuncts to other treatments. This review references guidelines from the Canadian Network for Mood and Anxiety Treatments and Michigan Medicine, along with an updated literature review, to provide a framework for reviewing medications or herbal formulation, as well as other therapies, which have evidence in the treatment of depression. In general, St. John's Wort, Omega-3 Fatty Acids, S-adenosyl-L-methionine, and crocus sativus (saffron) have the highest levels of evidence in the treatment of mild-to-moderate depression. Acetyl-l-carnitine, l-methylfolate, DHEA, and lavender have a moderate level of evidence in treating depression, whereas Vitamin D, one of the most common supplements in the United States, does not have evidence in treating depression. Of the non-medication-based therapies, exercise, light therapy, yoga, acupuncture, and probiotics have evidence in the treatment of depression, whereas a full review of dietary modifications for depression was out of scope for this article.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".