Combined chiropractic care and Tai Chi for chronic neck pain: A protocol for a pilot randomized trial
Bibliographic record
Abstract
Background: Neck pain presents a personal and socioeconomic burden globally. Despite increasing prevalence, research on chronic neck pain (CNP) is limited and management relies on generalized approaches. There is growing interest in non-pharmacological interventions, however their efficacy remains uncertain due to the multifactorial etiology of CNP. Two interventions, multimodal chiropractic care (MCC) and Tai Chi, have shown promise individually in managing CNP, and when combined may offer synergistic benefits. This pilot study aims to assess the feasibility of combining these interventions for CNP.Methods/design: Forty-eight adults, aged 18-65y, with CNP defined as occurring ≥5 days a week for ≥3 consecutive months, severity of ≥3 on the numeric rating scale, and a score of ≥5 on the Neck Disability Index will be recruited. Participants will be randomized 1:1:1 to one of the three treatment groups (MCC plus Tai Chi and Enhanced Usual Care (EUC), MCC plus EUC, or EUC alone). The MCC was validated using a modified Delphi approach. Primary outcomes relate to feasibility (recruitment, retention, and adherence) and secondary outcomes include clinical measures of neck pain severity and disability, health-related quality-of-life, psychosocial well-being, and physical function. Outcomes will be assessed at baseline, 16-weeks (post-intervention), and 24-weeks. Qualitative interviews will be conducted. Discussion: Results of this study will provide preliminary evidence regarding the feasibility and clinical evaluation of pragmatically delivered MCC, alone or in combination with Tai Chi, for individuals with CNP. These data will be used to inform the design of a fully powered, factorial trial evaluating two promising non-pharmacological therapies for CNP. Trial registration: This study is registered in ClinicalTrials.gov (NCT05726331).
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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 | 0.057 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.093 | 0.016 |
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".