Laser Acupuncture for Patients with Knee Osteoarthritis: A Systematic Review and Meta-Analysis of Randomized Placebo-Controlled Trials
Bibliographic record
Abstract
Objectives. To provide updated evidence from randomized controlled trials (RCTs) on the effectiveness of laser acupuncture for patients with knee osteoarthritis (KOA). Methods. A literature search in 9 databases was conducted from their inception through February 2019. Randomized controlled trials (RCTs) written in English that compared active laser acupuncture with placebo in KOA patients were included. Two authors independently extracted data from these trials. Meta-analysis software was used to analyze the data. Included studies were assessed in terms of the follow-up period, the methodological quality, and appropriateness of their technical features. Results. Of 357 studies, seven RCTs (totaling 395 patients) met the inclusion criteria. The short-term outcomes showed that laser acupuncture offered significant pain relief over placebo when assessed by the 100 mm visual analog scale (VAS) pain score ( p=0.02 ), while there was no significant difference between laser acupuncture and placebo based on Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain score ( p=0.25 ). For subgroup analysis, laser acupuncture had superiority over placebo in terms of both VAS and WOMAC pain scores in the appropriate technical features subgroup and the excellent methodological quality subgroup. But the effect of laser acupuncture on pain relief was not maintained in terms of either VAS ( p=0.19 ) or WOMAC pain score ( p=0.60 ). The pooled effect showed no significant difference between laser acupuncture and placebo at either time point according to WOMAC function scale, WOMAC stiffness scale, and quality of life outcome. Conclusions. Our findings indicate that laser acupuncture can effectively reduce knee pain for patients with KOA at short term when appropriate technical features are applied, but the effect likely fades away during the subsequent follow-up period.
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.029 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".