Sweet Solutions to Reduce Procedural Pain in Neonates: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Abundant evidence of sweet taste analgesia in neonates exists, yet placebo-controlled trials continue to be conducted. OBJECTIVE: To review all trials evaluating sweet solutions for analgesia in neonates and to conduct cumulative meta-analyses (CMAs) on behavioral pain outcomes. DATA SOURCES: (1) Data from 2 systematic reviews of sweet solutions for newborns; (2) searches ending 2015 of CINAHL, Medline, Embase, and psychINFO. DATA EXTRACTION AND ANALYSIS: Two authors screened studies for inclusion, conducted risk-of-bias ratings, and extracted behavioral outcome data for CMAs. CMA was performed using random effects meta-analysis. RESULTS: One hundred and sixty-eight studies were included; 148 (88%) included placebo/no-treatment arms. CMA for crying time included 29 trials (1175 infants). From the fifth trial in 2002, there was a statistically significant reduction in mean cry time for sweet solutions compared with placebo (-27 seconds, 95% confidence interval [CI] -51 to -4). By the final trial, CMA was -23 seconds in favor of sweet solutions (95% CI -29 to -18). CMA for pain scores included 50 trials (3341 infants). Results were in favor of sweet solutions from the second trial (0.5, 95% CI -1 to -0.1). Final results showed a standardized mean difference of -0.9 (95% CI -1.1 to -0.7). LIMITATIONS: We were unable to use or obtain data from many studies to include in the CMA. CONCLUSIONS: Evidence of sweet taste analgesia in neonates has existed since the first published trials, yet placebo/no-treatment, controlled trials have continued to be conducted. Future neonatal pain studies need to select more ethically responsible control groups.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".