Are tai chi and qigong effective in the treatment of TBI? A systematic review protocol
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) adversely affects both young and old and is a growing public health issue. A number of recent trends in managing TBI, such as recommending sub-threshold aerobic activity, tailoring multi-modal treatment strategies, and studying the possible role of low-grade inflammation in those with persistent symptoms, all suggest that the physical and cognitive exercise of tai chi/qigong could have benefit. Method: Designed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the following databases will be searched: MEDLINE, CINAHL, Cochrane Library, Embase, China National Knowledge Infrastructure Database, Wanfang Database, Chinese Scientific Journal Database, and Chinese Biomedical Literature Database. All clinical trials on mild, moderate and/or severe TBI with tai chi and/or qigong as the treatment group and any comparison group, in any setting will be included. Four reviewers will independently select studies; two reviewers for the English and two for the Chinese databases. Cochrane-based risk of bias assessments will be conducted on all included studies. An analysis will then be conducted with the grading of recommendation, assessment, development, and evaluation (GRADE) instrument. Results: This review will summarize the clinical trial evidence on tai chi/qigong for TBI including type of TBI, age/sex of participants, type and length of intervention and comparator, outcome measures, and any adverse events. The risk of bias will be considered, and the strengths and weaknesses of each trial will be analyzed. Discussion: PROSPERO [CRD42022364385].
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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 | 0.066 | 0.065 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.020 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 0.009 |
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".