The Effect of Intravascular Laser Irradiation of Blood on Serum Biomarkers and Clinical Outcome in Knee Osteoarthritis Patients: A Double-Blind Randomized Control Trial
Bibliographic record
Abstract
Knee osteoarthritis (OA) is a prevalent degenerative joint disease globally, causing pain, stiffness, and disability. Intravascular laser irradiation of blood (ILIB) has been used for chronic pain and musculoskeletal disease. However, evidence on the clinical benefits and serum biomarkers post-ILIB therapy in knee OA is insufficient. We designed a double-blind randomized controlled trial to evaluate the clinical and biological outcomes of ILIB therapy for knee OA. Seventeen patients with knee OA were randomly assigned to the ILIB and control groups. The outcomes included the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) Scale, visual analog scale, and biomarker analysis of interleukin (IL)-6, IL-13, IL-1β, epidermal growth factor, macrophage inflammatory protein-1β, and eotaxin. The measurements were performed at baseline and three days, one month, and three months post-intervention. The ILIB group showed a significant improvement in the WOMAC-pain score at one month of follow-up than the control group. IL-1β levels reduced significantly on day three, one month, and three months, and IL-13 levels reduced on day three and three months during follow-up in the ILIB group. ILIB therapy reduced knee OA pain for one month and significantly reduced serum IL-1β and IL-13 levels, suggesting potential for pain management.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".