Invasive and non-invasive acupuncture techniques for pain management in neonates: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it