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Record W2026032146 · doi:10.1542/peds.2006-0913r

Sucrose Analgesia: Identifying Potentially Better Practices

2006· article· en· W2026032146 on OpenAlexaff
Linda Lefrak, Kelly J. Burch, Rheta Caravantes, Kim Knoerlein, Nancy DeNolf, Jill Duncan, Frances Hampton, Céleste Johnston, Debbie Lockey, Cassandra Martin-Walters, Debra McLendon, Melinda Porter, Cliff Richardson, Cathy Robinson, Krystyna Toczylowski

Bibliographic record

VenuePEDIATRICS · 2006
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicinePain managementIntensive care medicineBest practiceAdverse effectBest evidenceAnesthesiaPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objectives of this study were to review the use of oral sucrose for procedural pain management in NICUs, develop potentially better practice guidelines that are based on the best current evidence, and provide ideas for the implementation of these potentially better practices. METHODS: A collaboration of 12 centers of the Vermont Oxford Network worked together to review the strength of the evidence, clinical indications, dosage, administration, and contraindications and identify potential adverse effects for the use of sucrose analgesia as the basis of potentially better practices for sucrose analgesia guidelines. Several units implemented the guidelines. RESULTS: Through reviews and inputs from all centers of the evidence, consensus was reached and guidelines that included indication, dosage per painful procedure, age-related dosage over 24 hours, method of delivery, and contraindications were developed. CONCLUSIONS: Guidelines now are available from a consensus group, and suggestions for implementation of guidelines, based on implementation of other pain management strategies, were developed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.154
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.154
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations79
Published2006
Admission routes1
Has abstractyes

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