Effectiveness of Tai Chi as a Therapeutic Exercise in Improving Balance and Postural Control
Bibliographic record
Abstract
Tai Chi (TC) is an ancient form of meditative exercise used to improve and maintain good health. To determine whether TC is effective in improving balance, a meta-analysis of the published literature was completed. Two investigators independently identified articles by searching 4 databases using the keywords: Tai Chi, balance, and postural control. Seven eligible articles were evaluated using a score-sheet that quantifies methodologic rigor developed by the investigators for this study; scores ranged from 33 to 43 (out of a maximum score of 49). For each article, the effect size of each outcome measure was quantified using the d-index (d). The mean d of measures corresponding to static conditions, internal perturbations, and external perturbations were -0.07 Q0.37, 1.52 []1.13, and 0.04 []0.40 respectively. Pearson's r between d and the score was []0.70, indicating a strong negative correlation between effect size and methodologic rigor. There is moderate research-based evidence to support the use of TC to improve balance and postural control as measured by responses to internal perturbations.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".