Investigation of the Therapeutic Effect and Mechanism of Holographic Meridian Scraping Therapy on Knee Osteoarthritis.
Bibliographic record
Abstract
Objective: This study aimed to investigate the efficacy of holographic meridian scraping therapy on patients with knee osteoarthritis (KOA) and its impact on serum IL-1β and TNF-α levels. Methods: A prospective study was conducted, enrolling seventy KOA patients admitted to the Hebei Provincial Hospital of Traditional Chinese Medicine between August 2021 and April 2022. The patients were divided into two groups using the random number table method: control group (n = 35) and treatment group (n = 35). The control group received oral celecoxib capsules (100 mg, twice daily), while the treatment group received an additional daily holographic meridian scraping session (20 minutes/day). Throughout the two-week study, the researchers continuously monitored the visual analogue scale (VAS) score, the Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and the changes in serum IL-1β and TNF-α expression. Results: The treatment group demonstrated significantly better overall efficiency and efficacy compared to the control group (P < .05). Both groups exhibited decreased VAS and WOMAC scores after treatment in comparison to pre-treatment levels (P < .05), with the treatment group showing lower scores than the control group after treatment (P < .05). Furthermore, serum TNF-α and IL-1β levels in both groups decreased after treatment compared to pre-treatment levels within the same group (P < .05). The treatment group had significantly lower serum TNF-α and IL-1β levels than the control group after treatment (P < .05). Conclusions: Combining holographic meridian scraping therapy with celecoxib effectively treats KOA and significantly improves patient conditions, along with reductions in serum TNF-α and IL-1β levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".