Invasive and non-invasive acupuncture techniques for pain management in neonates: a systematic review
Bibliographic record
Abstract
BACKGROUND: Neonatal pain is an extensive research field and there are many possibilities to treat pain in neonates. Acupuncture is one new and non-pharmacological option and a promising tool to reduce pain in neonates undergoing minor painful interventions during routine medical care. OBJECTIVES: This review summarises trials of acupuncture for pain reduction in neonates undergoing painful interventions during routine medical care. DATA SOURCE: MEDLINE, Embase, CINAHL, electronic clinical trials registry platforms and reference lists were systematically screened for trials from their dates of inception to February 2017 (English language database search). STUDY SELECTION: Inclusion criteria were (1) preterm or term neonates, (2) acupuncture for painful medical interventions and (3) formal pain assessment as a primary or secondary study outcome. We included only randomised controlled trials. DATA EXTRACTION: Data were extracted using a standardised protocol and individual risk of bias was assessed. RESULTS: The literature search revealed a total of 12 196 records. After application of inclusion criteria, five studies were included in this review. Two studies demonstrated significant pain reduction, one found equal outcomes in comparison to standard care, and two showed significantly higher pain scores with acupuncture alone. LIMITATIONS: =87%). CONCLUSION: The results of this review suggest that acupuncture may have a positive pain-relieving effect in neonates. However, due to the low number of available high-quality trials and heterogeneity across the studies it is not possible to state clear recommendations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".