Acupuncture combined with Tai Chi in the treatment of a patient with coccyx fracture: A case report
Bibliographic record
Abstract
RATIONALE: Coccyx fractures and subluxations are commonly caused by trauma, often leading to severe pain and restricted mobility. Traditional treatments focus on analgesia and immobilization; however, recovery periods are prolonged. This study explores the efficacy of acupuncture combined with Tai Chi in promoting rehabilitation for coccyx fractures. PATIENT CONCERNS: A 34-year-old female patient experienced sacrococcygeal pain and limited mobility after falling on her buttocks while skiing on February 13, 2025. DIAGNOSES: Computed tomography imaging indicated a suspected fracture of the S5 vertebral body, a possible subluxation of the Co1 vertebral body, and soft tissue swelling with effusion in the sacrococcygeal region. INTERVENTIONS: The treatment consisted of lumbosacral acupuncture, targeting the Baliao (Shangliao, Ciliao, Zhongliao, Xialiao) and Ashi points (local tender spots), supplemented by daily Five-Form Tai Chi exercises. Acupuncture sessions were conducted once daily, with needles retained for 10 minutes, over a consecutive period of 14 days. Furthermore, the Tai Chi exercises were performed twice daily, with each session lasting 20 minutes, also spanning a duration of 14 days. OUTCOMES: Post-treatment, the Visual Analog Scale pain score decreased from 7 to 3. Follow-up computed tomography revealed no obvious fractures on sacrococcygeal plain scans, with recommendations for follow-up or magnetic resonance imaging if necessary. The patient showed significant functional improvement and reduced anxiety. LESSONS: Acupuncture combined with Tai Chi effectively alleviates pain, promotes functional recovery, and accelerates fracture healing in patients with coccyx fractures and subluxations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| 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".