Effect of pine bark extract (Pycnogenol<sup>®</sup>) on symptoms of knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The safe and efficacious use of Pycnogenol (French maritime pine bark extract) in other inflammatory diseases prompted this study of its antiinflammatory effects in patients with osteoarthritis (OA). The aim of the study was to evaluate whether Pycnogenol reduces the symptoms of OA in a double-blind, placebo-controlled, randomly allocated trial with patients suffering from knee osteoarthritis stages I and II. METHODS: 100 patients were treated for 3 months either by 150 mg Pycnogenol per day at meals or by placebo. Patients had to report any change of use of previously prescribed antiinflammatory medication during the study period. Patients filled the Western Ontario and Mc Masters University (WOMAC) questionnaire for osteoarthritis every 2 weeks and evaluated weekly pain symptoms using a visual analogue scale for pain intensity. RESULTS: Following treatment with Pycnogenol patients reported an improvement of WOMAC index (p < 0.05), and a significant alleviation of pain by visual analogue scale (p < 0.04), the placebo had no effect. The use of analgesics diminished in the verum group but increased under the placebo. Treatment with Pycnogenol was well tolerated. CONCLUSION: Results show that Pycnogenol in patients with mild to moderate OA improves symptoms and is able to spare NSAIDs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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 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".