Bibliographic record
Abstract
Purpose: Osteoarthritic (OA) pain is largely considered to be inflammatory pain. However, during the last stage of knee OA, sen-sory nerve fibers in the knee are shown to be significantly damaged when the subchondral bone junction is destroyed, and this can induce neuropathic pain. Several authors have reported that tumor necrosis factor-α (TNFα) in a knee joint plays a crucial role in pain modulation. The purpose of the current study was to evaluate the efficacy of etanercept, a TNFα inhibitor, for pain in knee OA. Materials and Methods: Thirty-nine patients with knee OA and a 2–4 Kellgren-Lawrence grading were evaluated in this prospec-tive study. Patients were divided into two groups; hyaluronic acid (HA) and etanercept injection. All patients received a single in-jection into the knee. Pain scores were evaluated before and 4 weeks after injection using a visual analogue scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and they were compared between the groups. Results: Before injection, VAS and WOMAC scores were not significantly different between the groups (p>0.05). Significant pain relief was found in the etanercept group at 1 and 2 weeks by VAS, and at 4 weeks by WOMAC score, compared with the HA group (p<0.05). No adverse events were observed in either group. Conclusion: Direct injection of etanercept into OA knee joints was an effective treatment for pain in moderate and severe OA pa-tients. Furthermore, this finding suggests that TNFα is one factor that induces OA pain. Key Words: Etanercept, knee osteoarthritis, pain
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.799 | 0.765 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".