NEM® Brand Eggshell Membrane in the Treatment of Pain and StiffnessAssociated with Knee Osteoarthritis: An Open Label Clinical Study
Bibliographic record
Abstract
Abstract Objective: NEM® Brand Eggshell Membrane contains collagen and glycosaminoglycans that have beneficial effects in the treatment of Osteoarthritis (OA). A single-center, open-label clinical study was conducted to evaluate the efficacy and safety of NEM® in management of pain and stiffness associated with knee OA. Methods: Seventy subjects with knee OA received oral NEM® 500 mg once daily for 60 days. The primary outcome measure was to evaluate the effectiveness of NEM® in reducing pain and stiffness associated with knee OA. The primary endpoints were the change in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analog Scale for Pain (VAS pain) and Lequesne Algofunctional Index measured after 10, 30 and 60 days of NEM® supplementation. Results: NEM® treatment resulted in reduction of WOMAC overall scores at 10 days (16.6%, p=0.012), at 30 days (31.8%, p<0.0001) and at 60 days (46.7%, p<0.0001) post-treatment compared to baseline values. VAS pain was reduced at 10 days (19.6%, p<0.0001), at 30 days (31.8%, p<0.0001) and at 60 days (49.0%, p<0.0001). Overall Lequesne scores were reduced at 10 days (11.2%, p=0.0002), at 30 days (24.0%, p<0.0001) and at 60 days (36.8%, p<0.0001). In a Global Assessment, 68.6% of patients and 78.6% physicians rated the efficacy of NEM® as excellent or good. Three mild and transient, and no serious adverse events were reported. Conclusions: NEM® supplementation resulted in rapid and significant reduction of joint pain and stiffness (at 10 days) which were further improved at 60 days. NEM® treatment was safe and well tolerated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".