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Record W1462995830 · doi:10.1093/pch/16.10.629

The EPIQ evidence reviews – practical tools for an integrated approach to knowledge translation

2011· article· en· W1462995830 on OpenAlexaff
Shoo K. Lee, Nalini Singhal, Khalid Aziz, Catherine Cronin

Bibliographic record

VenuePaediatrics & Child Health · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of ManitobaUniversity of AlbertaUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsTranslation (biology)Computer scienceKnowledge translationData scienceKnowledge managementBiology

Abstract

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In this era of evidence-based medicine, the need for systematic reviews of the literature to facilitate knowledge translation is increasingly more evident (1,2). With vast quantities of new information being constantly published, clinicians with limited available time require comprehensive but succinct summaries of the literature to help guide patient management. Although there are many forms of systematic reviews, perhaps the best known is the Cochrane Collaboration and Library (3), which has become an invaluable and indispensible resource for health care practitioners worldwide. Cochrane reviews include rigorously performed clinical trials and are considered by many to be the ‘gold standard’ for systematic reviews. Members of the Canadian Neonatal Network recently developed the Evidence-based Practice for Improving Quality (EPIQ) method to provide high-quality health care for neonates (4,5). EPIQ is an evidence-based multidimensional approach to quality improvement aimed at changing organizational culture and sustaining behavioural change. It builds on traditional continuous quality improvement techniques by selectively targeting hospital-specific practices for intervention, thus, reducing the reliance on intuition and anecdotes that are associated with existing quality improvement methods. EPIQ is based on three pillars: use of evidence from the published literature; use of data from participating hospitals to target hospital-specific practices for intervention; and use of a national network to share expertise (5). To evaluate evidence from the published literature (Pillar 1), EPIQ used the International Liaison Committee on Resuscitation (ILCOR) approach (6). The ILCOR approach differs from the Cochrane approach because it adopts a broader systematic approach to the evaluation of evidence and evaluates all research related to a specific question, whether from clinical trials, observational data or animal studies. A worksheet is used to summarize the information from multiple sources according to level of evidence (Table 1) and their direction (supporting, opposing or neutral to the question) (7). This process significantly reduces the time required to conduct a critical review and negates the need for previous training in quantitative methods such as meta-analysis. The worksheet conclusions, the consensus on science and the treatment recommendations can be debated by experts with all the available evidence before them. Thus, the ILCOR approach complements the Cochrane method by including evidence from observational sources, simplifying the review process, generating consensus recommendations based on the best available evidence, and providing a pragmatic approach to guide the busy clinician in making clinical decisions. Levels of evidence (LOE) for therapeutic interventions Data adapted with permission from reference 9. RCTs Randomized controlled trials Levels of evidence (LOE) for therapeutic interventions Data adapted with permission from reference 9. RCTs Randomized controlled trials To meet the objectives of Pillar 2, coded observational data regarding practices and outcomes were collected from Canadian neonatal intensive care units on an ongoing basis to form the Canadian Neonatal Network Database. Data from this database were complemented by additional specific targeted data, and were used to provide information on practices and interventions that result in greater quality of care. Through cluster randomization experiments, clinical hypotheses could then be developed and tested in the clinical setting. Information from Pillars 1 and 2 were combined to develop practice change strategies. Finally, in Pillar 3, neonatal intensive care units collaborated to share their experiences with practice change strategies and their outcomes, and to encourage one another. In a cluster randomized controlled trial of Canadian neonatal intensive care units, EPIQ reduced bronchopulmonary dysplasia and nosocomial infection by 15% and 44%, respectively, although the latter did not reach statistical significance (5). The accompanying review on continuous positive airway pressure by Yee et al (8) (pages 633–637) is an example of how an EPIQ evidence review can be conducted and presented in a clear and succinct way. The authors concluded that although the existing literature does not provide conclusive evidence for use of continuous positive airway pressure in preterm infants, the available literature does suggest that there is sufficient evidence to support its use in certain circumstances, and they make recommendations for its use accordingly. Reviews of this nature provide clear, succinct and pragmatic guidance based on the best available evidence, which is what clinicians really need. It is, of course, necessary for the evidence to be periodically reviewed. In this regard, the evidence review worksheet proves extremely valuable because it can be readily updated when new information becomes available, and the conclusions and recommendations can be quickly revised and disseminated. Thus, unlike more academic models for evidence review, the EPIQ evidence review can provide a powerful, yet practical tool for knowledge translation and dissemination to the vast majority of health care practitioners, and is especially suitable for use in quality improvement. As part of a nationwide effort to improve quality of care in the neonatal intensive care unit, the Canadian Neonatal Network has established 30 evidence review teams comprising neonatologists, fellows, neonatal nurse practitioners, respiratory therapists and nurses at neonatal intensive care units across Canada. The 30 teams attended a national workshop in Barrie, Ontario, in November 2008, where they received training in the EPIQ evidence review method and met to identify current neonatal intensive care unit practice issues that could benefit from evidence reviews. They then reviewed the published evidence as part of an EPIQ quality improvement process. Each team was assigned specific topics for evidence review, and was given tasks and timelines. They defined the questions to be asked and the proposed review methodology, and submitted them to a central EPIQ Evidence Review Panel. Following approval by the panel, each team completed their assigned evidence reviews, which were then peer reviewed by selected members from other review teams. Practice recommendations were arrived at by debate and consensus using a peer group of clinicians. The completed evidence reviews will be submitted to Paediatrics & Child Health for peer review and publication. Summaries of the reviews will be published in the Journal, and the full reviews will be available from the EPIQ Library of Evidence Reviews on the public access EPIQ website at www.EPIQ.ca under ‘Evidence Reviews’. A training video is available on the same website for those who wish to learn more about how to conduct EPIQ evidence reviews. EPIQ evidence reviews have tremendous potential to contribute toward knowledge translation and evidence-based health care, and the method can be applied in all areas of health care. The authors thank the staff of the Canadian Neonatal Network EPIQ Study Coordinating Centre (Charles Ah Knit, Sukhy Mahl and Phillip Ye) for their tireless and diligent work.

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)0.350
metaresearch head score (Gemma)0.633
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.650
Threshold uncertainty score0.801

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3500.633
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0140.010
Bibliometrics0.0350.036
Science and technology studies0.0020.005
Scholarly communication0.0180.021
Open science0.0070.020
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0550.018

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 head0.900
GPT teacher head0.578
Teacher spread0.321 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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".

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Citations10
Published2011
Admission routes1
Has abstractyes

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