Reduced Health Resource Use After Acupuncture for Low-Back Pain
Bibliographic record
Abstract
OBJECTIVES: Acupuncture is commonly used to treat low-back pain (LBP) and clinical trials have demonstrated its efficacy. However, less is known about how the utilization of acupuncture impacts public health service utilization in the real world. This study investigates the association between acupuncture utilization for LBP and health care utilization by assessing whether patients who undergo acupuncture subsequently use fewer health care resources and whether those patients differ in their health care use from the general population with LBP. DESIGN: This study employed the design of a two-group pre/post secondary data analysis. SETTING AND SUBJECTS: There were two study populations. To identify patients who received acupuncture for LBP in 2000, patient charts at Alberta registered acupuncture clinics were reviewed. The comparison group was identified from the Alberta physician claims administrative database. Acupuncture group cases were matched with four comparison cases from the general population with LBP based on gender and age. OUTCOME MEASURES: Number of physician visits and physician service cost for LBP-related services for 1 year pre- and postacupuncture treatment period were calculated from the physician claims data for both study groups. RESULTS: For the 201 cases and 804 controls, the mean age was 48 years and 54% were female. The number of physician visits for the 1-year period postacupuncture decreased 49% for the acupuncture group (p<0.01) compared to the 1-year period preacupuncture. For the comparison there was a decrease of 2% in physician visits (p=0.59) for the same time periods. Corresponding to the decrease, physician services cost declined 37% for the case group (p=0.01) and 1% for the comparison (p=0.86). CONCLUSIONS: Results suggest that patients with LBP were less likely to visit physicians for LBP after acupuncture treatment. This led to reduced health services spending on LBP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".