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Record W2531766939 · doi:10.1111/jan.13182

Skin‐to‐skin contact for procedural pain in neonates: acceptability of novel systematic review synthesis methods and GRADEing of the evidence

2016· review· en· W2531766939 on OpenAlexafffund
Timothy Disher, Britney Benoit, Céleste Johnston, Marsha Campbell‐Yeo

Bibliographic record

VenueJournal of Advanced Nursing · 2016
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityNova Scotia Health AuthorityIzaak Walton Killam Health CentreDalhousie University
FundersKillam Trusts
KeywordsMedicineRandomized controlled trialMeta-analysisConfidence intervalCochrane LibraryPhysical therapyMEDLINESystematic reviewStrictly standardized mean differenceHeelSurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.507
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.455
Teacher spread0.389 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2016
Admission routes2
Has abstractyes

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