Tai Chi Improves Physical Function in Older Chinese Women With Knee Osteoarthritis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
BACKGROUND: Tai chi (TC) is proposed as a potential option for the management of osteoarthritis (OA), however, its beneficial effect on patients with knee OA has not been convincing. OBJECTIVES: To evaluate the effect of a 24-week TC program on physical functions in older Chinese women with knee OA. METHODS: Thirty-five older Chinese women with knee OA were randomized into TC group (n = 18) and attention control (wellness education and stretching) group (n = 17). Subjects in the TC group practiced the 24-form simplified Yang-style TC 2 to 4 times a week for 24 weeks with frequency gradually increased. Physical function was assessed using the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), 6-minute walk distance and stair climb time. RESULTS: Compared with the control group, the participants in TC group had statistically significant improvements in changes of the WOMAC total score (6.18 +/- 2.13 vs. 1.71 +/- 2.73, P = 0.000), the WOMAC pain subscale (1.36 +/- 0.22 vs. 0.07 +/- 1.00, P = 0.001), the WOMAC stiffness subscale (0.66 +/- 0.25 vs. 0.05 +/- 0.38, P = 0.043), the WOMAC function subscale (6.17 +/- 1.96 vs. 1.72 +/- 2.63, P = 0.000), the 6-minute walk distance (32.43 +/- 14.20 vs. 6.67 +/- 16.76, P = 0.003), and the stair climb time (2.27 +/- 0.74 vs. 0.27 +/- 1.24, P = 0.001). CONCLUSIONS: This study suggests that TC provides a safe, feasible and useful exercise option for older Chinese female patients with knee OA.
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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".