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Record W2080526315 · doi:10.1097/ccm.0b013e3182982b03

Knowledge Translation Interventions for Critically Ill Patients

2013· review· en· W2080526315 on OpenAlexafffund
Tasnim Sinuff, John Muscedere, Neill K. J. Adhikari, Henry T. Stelfox, Peter Dodek, Daren K. Heyland, Gordon D. Rubenfeld, Ruxandra Pinto, Venika Manoharan, Jan Currie, Naomi E. Cahill, Jan O. Friedrich, André Carlos Kajdacsy-Balla Amaral, Dominique Piquette, Damon C. Scales, Sonny Dhanani, Allan Garland

Bibliographic record

VenueCritical Care Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsChildren's Hospital of Eastern OntarioWinnipeg Regional Health AuthorityMcMaster UniversitySt. Michael's HospitalSunnybrook Health Science CentreCentre for Advancing Health OutcomesKingston General HospitalUniversity of CalgaryProvidence Health CareKingston Health Sciences CentreAlberta Health ServicesQueen's UniversityHealth Sciences CentreUniversity of ManitobaClinical Evaluation Research UnitUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineObservational studyRandomized controlled trialPsychological interventionKnowledge translationData extractionMEDLINEIntensive careIntensive care medicineIntervention (counseling)Clinical trialNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: We systematically reviewed ICU-based knowledge translation studies to assess the impact of knowledge translation interventions on processes and outcomes of care. DATA SOURCES: We searched electronic databases (to July, 2010) without language restrictions and hand-searched reference lists of relevant studies and reviews. STUDY SELECTION: Two reviewers independently identified randomized controlled trials and observational studies comparing any ICU-based knowledge translation intervention (e.g., protocols, guidelines, and audit and feedback) to management without a knowledge translation intervention. We focused on clinical topics that were addressed in greater than or equal to five studies. DATA EXTRACTION: Pairs of reviewers abstracted data on the clinical topic, knowledge translation intervention(s), process of care measures, and patient outcomes. For each individual or combination of knowledge translation intervention(s) addressed in greater than or equal to three studies, we summarized each study using median risk ratio for dichotomous and standardized mean difference for continuous process measures. We used random-effects models. Anticipating a small number of randomized controlled trials, our primary meta-analyses included randomized controlled trials and observational studies. In separate sensitivity analyses, we excluded randomized controlled trials and collapsed protocols, guidelines, and bundles into one category of intervention. We conducted meta-analyses for clinical outcomes (ICU and hospital mortality, ventilator-associated pneumonia, duration of mechanical ventilation, and ICU length of stay) related to interventions that were associated with improvements in processes of care. DATA SYNTHESIS: From 11,742 publications, we included 119 investigations (seven randomized controlled trials, 112 observational studies) on nine clinical topics. Interventions that included protocols with or without education improved continuous process measures (seven observational studies and one randomized controlled trial; standardized mean difference [95% CI]: 0.26 [0.1, 0.42]; p = 0.001 and four observational studies and one randomized controlled trial; 0.83 [0.37, 1.29]; p = 0.0004, respectively). Heterogeneity among studies within topics ranged from low to extreme. The exclusion of randomized controlled trials did not change our results. Single-intervention and lower-quality studies had higher standardized mean differences compared to multiple-intervention and higher-quality studies (p = 0.013 and 0.016, respectively). There were no associated improvements in clinical outcomes. CONCLUSIONS: Knowledge translation interventions in the ICU that include protocols with or without education are associated with the greatest improvements in processes of critical care.

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)0.000
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.215
GPT teacher head0.499
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 teacher head, not a consensus.

Study designNot applicable
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

Citations80
Published2013
Admission routes2
Has abstractyes

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