Effect of acupuncture therapies combined with usual medical care on knee osteoarthritis.
Bibliographic record
Abstract
OBJECTIVE: To observe the effect of acupuncture or electroacupuncture (EA) combined with usual medical care for treating knee osteoarthritis (KOA) . METHODS: A total of 90 patients with KOA were randomly allocated to 3 groups: usual care group (UC group, n = 30) was treated by pharmacological treatment of non-steroidal anti-inflammatory drugs (NSAIDs) and drugs for activating blood circulation (Ds-ABC), acupuncture (AP) combined with usual care group (UC group) (AP + UC group, n = 30) and EA combined with UC group (EA + UC group, n = 30). The primary outcome measurements included pain visual analogue scale/score (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC Index) and its subscales. Secondary outcome measurement was Assessment of Quality of Life instrument version of the 36-item Short Form Health Survey (AQoL-SF36). RESULTS: By the end of the 1st week, AP + UC group and EA + UC group exhibited statistically significant improvements in primary outcome measures, except for WOMAC stiffness, compared with the UC group (P < 0.05). Moreover, the energy/fatigue domain of AQoL-SF36 in the AP + UC group showed better results than UC group (P < 0.05). By the end of the 2nd week, all the primary outcome measures revealed that either the AP + UC or EA + UC group demonstrating remarkable advantages compared with the UC group (P < 0.05). The social functioning and general health domains of AQoL-SF36 in the two acupuncture-intervention groups were improved significantly than UC group (P < 0.05). We also found the energy/fatigue and emotional wellbeing domains of AQoL-SF36 in the EA + UC group demonstrated better results than UC group (P < 0.05). CONCLUSION: AP or EA combined with usual care is more effective than usual care alone for the treatment of KOA, the intervention of electric current in the process of acupuncture may improve more domains of AQoL-SF36 in KOA patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".