The Use of Safflower (Carthamus tinctorius) in Treating Depression and Anxiety
Bibliographic record
Abstract
Objective In the era of evidence-based medicine, research in the area of herbal psychopharmacology has increased dramatically in recent decades. To date, however, there is no comprehensive review of safflower as an herbal antidepressant and anxiolytic with details on its psychopharmacology and applications in depression and anxiety. Methods This research is a review and qualitative research through an electronic survey among the Saudi population, thus assessing their knowledge about using safflower in treating depression and anxiety. The survey was distributed in Saudi Arabia in December 2021 and the results were finalized in January 2022. Results A total of 1074 Saudi participants were included in the study; 1002 (93.3%) participants reported knowing safflower very well while 72 (6.7%) had never heard of it. Some participants had used safflower infusions to treat anxiety and depression; 446 (44.4%) participants had never used it, but the remaining 558 (55.6%) had used it to varying degrees to treat anxiety and depression. Among the 752 participants who previously tried safflower, 279 (37.1%) reported that safflower was very effective, 389 (51.73%) reported some improvement, and 93 (12.36%) reported no improvement. Conclusion Emerging medical evidence is guiding herbal treatments. This research illustrates that more than 75% of the Saudi population are using Safflower to treat psychological stress. It elaborates that more than half of the population are already using safflower off the label to treat depression and anxiety and that they find it useful. A well-constructed clinical trial is thus critical to prove the evidence-based benefits of safflower in treating depression and anxiety. More studies on possible side effects are required to guarantee its safety. Nature has previously provided remarkable remedies, and more work will illustrate the value of safflower.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".