Acupuncture in Patients with a Vertebral Compression Fracture: A Protocol for a Randomized, Controlled, Pilot Clinical Trial
Bibliographic record
Abstract
OBJECTIVES: A vertebral compression fracture (VCF) is characterized by back pain and fracture of a vertebral body on spinal radiography. VCFs of the thoraco lumbar spine are common in the elderly. In general, appropriate analgesics should be prescribed to reduce pain and, thus, promote early mobilization. The ideal treatment approach for VCFs has not been determined. In Korea, acupuncture and herbal medication have been used to treat VCFs for many years. There is empirical evidence that acupuncture might benefit patients with a VCF. However, no randomized, controlled, clinical trials evaluating the efficacy and the safety of acupuncture for treating a VCF have been published. Therefore, we designed a randomized, controlled, pilot, clinical trial to obtain information for the design of a further full scale trial. METHODS: A five week protocol for a randomized, controlled, pilot, clinical trial is presented. Fourteen patients will be recruited and randomly allocated to two groups: a control group receiving interlaminar epidural steroid injections once a week for three weeks, and an experimental group receiving interlaminar epidural steroid injections plus acupuncture treatment (three acupuncture sessions per week for three weeks, nine sessions in total). The primary outcomes will be the pain intensity (visual analogue scale and PainVision(TM) system). The secondary outcome measurements will be the answers on the short form McGill pain questionnaire and the oswestry disability index. Assessments will be made at baseline and at one, three, and five weeks. The last assessment (week five) will take place two weeks after treatment cessation. This study will provide both an indication of feasibility and a clinical foundation for a future large scale trial. The outcomes will provide additional resources for incorporating acupuncture into existing treatments, such as nonsteroidal anti-inflammatory medications, narcotics and vertebral augmentation. This article describes the protocol.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".