Acupuncture as a Nonpharmacologic Treatment for Pain in a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVES: With epidemic opioid deaths and abuse in the United States, government agencies recommend nonpharmacological treatments for pain. However nonopioid treatment options for moderate to severe pain in the pediatric emergency department (PED) are limited. Acupuncture has been shown to be effective for pain. The objective of this study was to evaluate the feasibility of using traditional acupuncture (TA) and battlefield acupuncture (BFA) in the treatment of pain in the PED. METHODS: A pediatric cohort treated with acupuncture for pain in an urban PED was assessed. All subjects received TA or BFA as treatment, and pre/postacupuncture pain scores, feedback, and adverse events were assessed. The primary outcome was a change in pain score. RESULTS: Twelve patients received BFA, and 13 received TA for these pain conditions: headaches, sciatica, paraphimosis, torticollis, joint pains (knee, shoulder, jaw), sprains (foot, wrist, thumb), dysmenorrhea, otitis externa, sickle cell, and muscle knot. The mean ± SD pain score change, 5.8 ± 2.5 (P < 0.05; 95% confidence interval, 4.9-7.0), was clinically and statistically significant. Over 90% of subjects reported significant improvement or resolution of pain; 96% were satisfied with pain relief and would receive acupuncture again. Two adverse events were noted: one patient reported dizziness, and another, a tinge of blood at 1 of 90 needled points. CONCLUSIONS: This study suggests that acupuncture is a potential nonpharmacologic therapeutic option for acute pain management in the PED.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".