Adaptogenic Potential of a Polyherbal Natural Health Product: Report on a Longitudinal Clinical Trial
Bibliographic record
Abstract
Stress is a risk factor for a number of diseases and is an important predictor of health in general. Herbal medicines have been used as adaptogens to regulate and improve the stress response and there is evidence to support the use of herbal medicines for this purpose. We conducted an open-label longitudinal study on the natural health product, OCTA(c), a compound mixture of eight herbs, to determine its effects on perceptions of stress. Eighteen participants were enrolled in the study and were followed over a period of 3 months. Primary endpoints included scores from four validated questionnaires (SF-36v2, PSS, STAI and BDI-II), serum DHEA, ALT, AST and creatinine all measured at 12 weeks. Seventeen patients completed the study. Except for the physical summary score of the SF36 questionnaire, all the subjective scores indicated a highly significant (P < 0.0001) improvement in the participants' ability to cope with stress. No adverse effects were reported and there was no evidence of damage to the liver or kidney based on serum markers. Initial evidence for this polyherbal compound supports its potential as an effective 'adaptogenic' aid in dealing with stress. Further research using a randomized controlled design is necessary to confirm the findings from this pilot study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".