Skin‐to‐skin contact for procedural pain in neonates: acceptability of novel systematic review synthesis methods and GRADEing of the evidence
Bibliographic record
Abstract
AIMS: The aim of this study was to assess the acceptability of methods that increase trial inclusion in meta-analyses, and the level of evidence for skin-to-skin contact for procedural pain in infants. BACKGROUND: The current Cochrane review of skin-to-skin contact for pain in newborns found it to be an effective intervention, but identified several methodological limitations. DESIGN: Meta-re-analysis METHODS: Trial designs included randomized trials reporting a validated pain assessment tool as a primary outcome including term and pre-term infants undergoing a tissue-breaking painful procedure. The search in the original review was conducted to January 2013. Scores of validated tools were scaled to the premature infant pain profile in a fixed-effect meta-re-analysis. The GRADE was used to assess quality of meta-analysed evidence. RESULTS: New analysis vs. original found a mean difference: -3·11 in favour of skin-to-skin contact vs. -3·21 at 30 seconds; and -2·71 vs. -1·85 at 60 seconds for heel lance. Based on cut-off scores for the Neonatal Infant Pain Scale, infants receiving skin-to-skin contact during IM injection were more likely to display low pain after injection; and during recovery. CONCLUSION: Scaling scores to a single outcome can provide additional information in meta-analyses, simplifies interpretability of pooled scores, and can improve GRADE outcomes. Sensitivity analyses of scaled scores improve confidence in their validity. Risk of bias subgroups simplified the GRADE process, and confidence intervals for heterogeneity statistics assisted in interpretation of sensitivity analyses.
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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 | 0.455 | 0.726 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".