Sweet Solutions for Analgesia in Neonates in China: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: High-quality synthesized evidence of sweet taste analgesia in neonates exists. However, Chinese databases have never been included in previous systematic reviews of sweet solutions for procedural pain. OBJECTIVE: To conduct a systematic review of Chinese literature evaluating analgesic effects of sweet solutions for neonates. Data sources: Wang Fang, China National Knowledge Infrastructure and Chinese Biomedical Literature Database. Data extraction and analysis: Two authors screened studies for inclusion and conducted risk of bias ratings and data extraction. A third author resolved any conflicts. Meta-analyses were performed using RevMan 5.2 software, on mean differences in pain outcomes using random effects models. RESULTS: Thirty-one trials (4999 neonates) were included; 26 trials used glucose, 4 used sucrose, and 1 trial evaluated both solutions. Sweet solutions reduced standardized mean pain scores (n = 21 studies; -1.68, 95% confidence interval -2.08, -1.27) and cry duration (n = 6 studies; -25.60, 95% confidence interval -36.47, -14.72 s) but not heart rate change (n = 7 studies; -17.64, 95% confidence interval -52.71, 17.43). No included studies cited the previously published systematic reviews of sweet solutions. CONCLUSIONS: This systematic review of Chinese databases showed the same results as previously published systematic reviews. No trials included in this review cited the English systematic reviews, highlighting a parallel research agenda.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".