Single or Multiple Electroacupuncture Sessions in Nonspecific Low Back Pain: Are We Low-Responders to Electroacupuncture?
Bibliographic record
Abstract
The objective of this study was to compare the effects of one or multiple sessions of electroacupuncture (EA) in patients with chronic low back pain. The outcome measures were visual analog score (VAS), pressure pain threshold (PPT), McGill pain questionnaire (MPQ), Roland Morris disability questionnaire (RMDQ), low back skin temperature, surface electromyography of longissimus muscle (contraction/rest) and blood cytokines. After examination (AV0), patients were submitted to EA (2 Hz, 30 minutes, bilaterally at the SP6, BL23, BL31, BL32, BL33, and BL60) and were revaluated after one week (AV1). Patients with VAS <3 (VAS <3 group, n = 20) were directed to return after three weeks (AV2). Patients with VAS >3 (VAS >3 group, n = 20) were submitted to one weekly EA-treatment and revaluated after three weeks (AV2). The VAS <3 group showed a significant reduction in VAS and MPQ and increased PPT in AV1, but not in AV2. No significant differences were found in RMDQ. The VAS >3 group showed reduction in VAS and increased PPT in AV1 and a reduction in MPQ and RMDQ only in AV2. No significant differences were found in electromyography, temperature or cytokines. Thus, despite 2Hz-EA is effective reducing low back pain, some patients only experienced reduced pain intensity and improved functional capacity after full treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".