Collagen supplementation for rheumatoid arthritis and osteoarthritis
Bibliographic record
Abstract
We read with great interest the article by Jabbari et al. on the role of collagen supplementation in rheumatoid arthritis (RA) and osteoarthritis (OA).1 We appreciate the authors' valuable contribution to this comprehensive systematic review; however, we would like to highlight some key points. First, although the authors extracted information on different types of collagen such as intact, hydrolyzed, type I and type II collagen was extracted, there was a lack of details on residual confounders such as concurrent vitamin C (Vit C) supplementation. Vit C plays a crucial role in the absorption and production of collagen in the human body and its deficiency can affect the formation of a mature collagen network.2, 3 Specifically, maintaining a normal mature collagen network in humans depends on the anti-scurvy properties of Vit C, which prevent the auto-inactivation of the two key enzymes in collagen biosynthesis, lysyl and prolyl hydroxylase.2 Age and collagen-related food intake or nutritional support,4 which can influence the rate and amount of collagen production, are also significant residual confounders that should be considered.5 Likewise, effect measure modification by common comorbidities or risk factors for RA and OA of inflammatory or autoimmune conditions,6, 7 including psoriasis,8, 9 psoriatic arthritis,10, 11 spondyloarthritis,12 irritable bowel syndrome,13 periodontitis or other oral diseases,7, 14-21 fibromyalgia,22 and obstructive sleep apnea,23, 24 may be elucidated to identify populations that may benefit from collagen supplementation.To better understand the safety and adverse effects of collagen supplementation, it is important to consider potential confounders, such as the concomitant use of medications. This would be essential in determining whether the observed toxicities were a result of collagen supplementation or co-medications. To aid in interpreting safety profiles, algorithms for causality assessment of adverse events, similar to those used in research on drug toxicity and adverse drug reactions,25-27 can be applied. Therefore, further subgroup analysis or adjustment of these confounders may be necessary to address this issue. Second, while the included studies used the American College of Rheumatology Classification Criteria and Western Ontario and McMaster Universities Arthritis Index for the assessment of RA and OA, respectively, there are other clinically significant protocols or indexes that can be applied in different circumstances. For instance, the 28-joint Disease Activity Score (DAS28) is a useful tool for assessing disease activity in patients with RA, as it evaluates 28 tender and swollen joints, general health, and levels of acute phase reactants such as erythrocyte sedimentation rate or C-reactive protein.28-33 Similarly, the Knee Injury and Osteoarthritis Outcome Score (KOOS) is a suitable measurement tool for assessing OA in young and old adults, with adequate internal consistency, test–retest reliability, and construct validity.34-37 Furthermore, the Lysholm score and the International Knee Documentation Committee (IKDC) Subjective Knee Form are reliable and valid instruments that should be considered, especially for patients with OA related to ligament or meniscus injury.38, 39 In conclusion, to improve patient care and patient education,22, 40, 41 future studies should address important residual confounders and implement clinically significant protocols to provide more evidence for managing RA and OA. This research was not sponsored by a specific project grant. The authors declare no conflicts of interest. All authors provided their ideas and critical contribution. YC and CH contributed to the writing and editing of the manuscript. YC and CH contributed equally as first authors. KC and KSM contributed equally as corresponding authors.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".