A randomized, double‐blind, placebo‐controlled exploratory study to evaluate the potential of pycnogenol<sup>®</sup> for improving allergic rhinitis symptoms
Bibliographic record
Abstract
The potential of Pycnogenol for relieving allergic rhinitis (birch pollen) symptoms was explored in a double-blind, placebo-controlled trial. In 2008 19 subjects started treatment 3 weeks prior to the onset of birch pollen season in Ontario, Canada. While there was an improvement of eye and nasal symptoms with Pycnogenol, there was no significance versus placebo. It was postulated that Pycnogenol may require a lag-time between the start of therapy and the onset of action. Therefore 39 subjects were treated 5-8 weeks prior to the 2009 birch allergy season. The evaluation of subjects in 2009 showed much lower scores for eye (-35%) and nasal (-20.5%) symptoms with Pycnogenol compared with placebo. In succession of the allergy season birch specific IgE increased by 31.9% in the placebo group compared with only 19.4% in the Pycnogenol group. Detailed analysis suggested that symptom-relief was better the longer subjects were on Pycnogenol prior to the allergen exposure. The best results were found with subjects who took Pycnogenol 7-8 weeks ahead of the allergy season. With the limited number of 39 patients statistical predications were unattainable. In conclusion, Pycnogenol improved allergic rhinitis symptoms when supplementation was started at least 5 weeks before the onset of the allergy season.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".