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Record W4310331488 · doi:10.1097/cce.0000000000000794

Academic and Community ICUs Participating in a Critical Care Randomized Trial: A Comparison of Patient Characteristics and Trial Metrics

2022· article· en· W4310331488 on OpenAlexaffabout
Jennifer Tsang, Alexandra Binnie, Erick Duan, Jennie Johnstone, Diane Heels‐Ansdell, Brenda Reeve, Sébastien Trop, Paul Hosek, Joanna C. Dionne, Patrick Archambault, Paul Lysecki, Robert Cirone, Nicole Zytaruk, William Dechert, Mercedes Camargo, Rebecca Jesso, Elliot McMillan, Zaynab Panchbhaya, Tracy Campbell, Lois Saunders, Mary Copland, Kanthi Kavikondala

Bibliographic record

VenueCritical Care Explorations · 2022
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreJoseph Brant HospitalCanadian Institutes of Health ResearchImpactCégep de LévisUniversity of TorontoBrantford Energy (Canada)Sinai Health SystemSt. Joseph’s Healthcare HamiltonNiagara Health SystemWilliam Osler Health SystemGrand River HospitalMcMaster University
Fundersnot available
KeywordsRandomized controlled trialIntensive careMedicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Clinical research in Canada is conducted primarily in “academic” hospitals, whereas most clinical care is provided in “community” hospitals. The objective of this nested observational study was to compare patient characteristics, outcomes, process-of-care variables, and trial metrics for patients enrolled in a large randomized controlled trial who were admitted to academic and community hospitals in Canada. DESIGN: We conducted a preplanned observational study nested within the Probiotics: Prevention of Severe Pneumonia and Endotracheal Colonization Trial (PROSPECT, a randomized controlled trial comparing probiotics to placebo in mechanically ventilated patients) Research Program. SETTING: ICUs. PATIENTS: Mechanically ventilated patients. MEASUREMENTS: We compared patient characteristics, interventions, outcomes, and trial metrics between patients enrolled in PROSPECT from academic and community hospitals. MAIN RESULTS: Participating centers included 34 (82.9%) academic and seven (17.1%) community hospitals, which enrolled 2,203 (86.2%) and 352 (13.8%) patients, respectively. Compared with academic hospitals, patients enrolled in community hospitals were older (mean [ sd ] 62.7 yr [14.9 yr] vs 59.5 yr [16.4 yr]; p = 0.044), had longer ICU stays (median [interquartile range {IQR}], 13 d [8–23 d] vs 11 d [7–8 d]; p = 0.012) and higher mortality (percentage, [95% CI] in the ICU, 30.4% [25.8–35.4%]vs 20.5% [18.9–11.3%]; p = 0.002) and hospital (40.6% [35.6–45.8%] vs 26.1% [24.3–27.9%]; p < 0.001). Trial metrics, including informed consent rate (85.9% vs 76.3%; p = 0.149), mean ( sd ) monthly enrolment rate (2.1 [1.4] vs 1.1 [0.7]; p = 0.119), and protocol adherence (90.6% vs 91.6%; p = 0.207), were similar between community and academic ICUs. CONCLUSIONS: Community hospitals can conduct high-quality research, with similar trial metrics to academic hospitals. Patient characteristics differed between community and academic hospitals, highlighting the need for broader engagement of community hospitals in clinical research to ensure generalizability of study results.

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)0.022
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.199
GPT teacher head0.468
Teacher spread0.270 · 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.

Study designObservational
DomainEvaluation
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

Citations15
Published2022
Admission routes2
Has abstractyes

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