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Record W2027327104 · doi:10.1097/ccm.0b013e3181921079

Investigator-led clinical research consortia: The Canadian Critical Care Trials Group

2008· article· en· W2027327104 on OpenAlexaffabout
John C. Marshall

Bibliographic record

VenueCritical Care Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMultidisciplinary approachIntensive care unitClinical trialNatural historyIntensive care medicineIntensive careCritically illClinical researchConvictionNursingFamily medicinePathology

Abstract

fetched live from OpenAlex

Advances in the care of critically ill patients are dependent upon rigorous clinical research undertaken to characterize natural history and risk factors, and determine optimal approaches to the management of the diseases of the critically ill patient. The Canadian Critical Care Trials Group (CCCTG) was formed in 1989 to foster such research. It has grown to become a national, multidisciplinary organization with more than 100 members, and more than 3 dozen active research programs. Its members have been highly successful in obtaining funding for, completing, and publishing well-designed studies that have informed international practice in areas such as transfusion, stress ulcer prophylaxis, long term outcomes from acute respiratory distress syndrome, diagnosis and management of infection in the intensive care unit, and end-of-life care. In the process, the CCCTG has developed a highly effective culture of scientific mentoring, and has served as a model for investigator-led critical care research groups around the world. This review summarizes the history, activities, approaches, and challenges of the CCCTG, in the conviction that investigator-led groups such as ours represent the future of intensive care unit-based research.

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)0.005
metaresearch head score (Gemma)0.159
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.159
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.009
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.624
GPT teacher head0.577
Teacher spread0.047 · 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; both teacher heads agree on what is shown here.

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

Citations49
Published2008
Admission routes2
Has abstractyes

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