Effectiveness of collagen supplementation on pain scores in healthy individuals with self-reported knee pain: a randomized controlled trial
Bibliographic record
Abstract
The purpose of this study was to examine the effects of 12 weeks collagen peptide (CP) supplementation on knee pain and function in individuals with self-reported knee pain. Healthy physically active individuals (n = 167; aged 63 [interquartile range = 56–68] years) with self-reported knee pain received 10 g/day of CP or placebo for 12 weeks. Knee pain and function were measured with the Visual Analog Scale (VAS), the Lysholm questionnaire, and the Knee injury and Osteoarthritis Outcome Score (KOOS). Furthermore, we assessed changes in inflammatory, cartilage, and bone (bio)markers. Measurements were conducted at baseline and after 12 weeks of supplementation. Baseline VAS did not differ between CP and placebo (4.7 [2.5–6.1] vs. 4.7 [2.8–6.2], p = 0.50), whereas a similar decrease in VAS was observed after supplementation (−1.6 ± 2.4 vs. −1.9 ± 2.6, p = 0.42). The KOOS and Lysholm scores increased after supplementation in both groups (p values < 0.001), whereas the increase in the KOOS and Lysholm scores did not differ between groups (p = 0.28 and p = 0.76, respectively). Furthermore, CP did not impact inflammatory, cartilage, and bone (bio)markers (p values > 0.05). A reduced knee pain and improved knee function were observed following supplementation, but changes were similar between groups. This suggests that CP supplementation over a 12-week period does not reduce knee pain in healthy, active, middle-aged to elderly individuals. Novelty CP supplementation over a 12-week period does not reduce knee pain in healthy, active, middle-aged to elderly individuals. CP supplementation over a 12-week period does not impact on inflammatory, cartilage, and bone (bio)markers in healthy, active, middle-aged to elderly individuals.
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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) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".