A review of the Experimental Approaches Used in Clinical Studies to Evaluate the Health Benefits of Plant Food Supplements Associated With Infectious Diseases
Bibliographic record
Abstract
The objective of this review was to evaluate the experimental approaches used in clinical trials to support the benefit claims of plant food supplements (PFS) with reported activity on infectious diseases. A literature search was conducted on a list of 309 plant species currently used in food supplements in Europe using the National Centre for Biotechnology Information (NCBI) PubMed Database to identify all the clinical trials for evaluating the benefit claim for preventing or treating infectious diseases in humans. The searches included a combination of terms related to the name of plants, class of infectious agents and therapeutic activities against infectious disease. By limiting the searches to clinical studies, only 27 articles representing 19 plant species were identified. From this list, 13 papers from the 10 plants most extensively researched were critically evaluated for assessing methods used to assess the benefits of PFS. Different study designs were used ranging from an open trial with no placebo or control and no randomization to double-blind randomized placebo controlled trials including a crossover design. Although the experimental approaches described in this review were found to be suitable for evaluating the benefit claims of PFS for treating infectious diseases caused by bacteria, viruses, fungi and parasites, the clinical study design should be more standardised as many studies lacked a control group and sufficient population size to be statistically acceptable taking into consideration patient variability. The reporting of the results varied and should also be standardised to include all the study parameters and data collected.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".