Abstracts and Commentaries on Key Articles in the Literature
Bibliographic record
Abstract
T he aim of this study, performed by researchers from Australia and Canada, was to determine the safety and feasibility of auricular noninvasive magnetic acupuncture (MA) to decrease infant pain during heel pricks.Infants requiring heel pricks for blood collection were randomized to either MA (n = 21) or placebo (P; n = 19) after parental informed consent.MA or placebo stickers were placed on both ears according to the Battlefield Protocol by an unblinded investigator and left on for 3 days.Pain was assessed with the Premature Infant Pain Profile (PIPP) by blinded clinicians.The results of the study were the following: Mean gestation (MA: 34.1 weeks; P: 34.4 weeks) and age of infants (MA: 5.3 days; P: 4.5 days) were similar as were mean (standard deviation [SD]) pre (MA: 1.7 [1.4]; P: 2.1 [1.9]) and post (MA: 1.6 [1.4]; P: 2.1[1.7])heel prick PIPP scores.PIPP scores were significantly lower in MA infants during heel pricks (MA: 5.9 [3.7]; P: 8.3 [4.7]; P = 0.04).One-way analysis of covariance modeling showed that MA was significantly associated with lower PIPP scores after controlling for analgesic use (P = 0.043).No differences in heart rate, oxygen saturation, analgesic use, or adverse effects (e.g., local skin reactions) were noted.This pilot study shows that auricular MA is feasible in neonates and can reduce PIPP scores during heel pricks.Further study is required to determine the impact of MA on other painful or stressful conditions and on neurodevelopment.
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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 | 0.027 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.030 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.246 | 0.086 |
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".