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Record W3109761717 · doi:10.1016/s2213-2600(20)30471-9

Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?

2020· review· en· W3109761717 on OpenAlexafffund
Christopher J. Yarnell, Darryl Abrams, Matthew R. Baldwin, Daniel Brodie, Eddy Fan, Niall D. Ferguson, May Hua, Purnema Madahar, Laveena Munshi, Gavin D. Perkins, Gordon D. Rubenfeld, Arthur S. Slutsky, Hannah Wunsch, Robert Fowler, George Tomlinson, Jeremy R. Beitler, Ewan C. Goligher

Bibliographic record

VenueThe Lancet Respiratory Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsInstitute for Clinical Evaluative SciencesToronto General HospitalUniversity of TorontoUniversity Health NetworkSunnybrook Health Science CentreSinai Health SystemToronto Rehabilitation InstituteHealth Sciences CentreMount Sinai Hospital
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingInnovate UKMedical Research CouncilCanadian Institutes of Health ResearchAmerican Federation for Aging ResearchNational Institute for Health and Care ResearchDepartment of Health and Social CareWellcome TrustNational Institutes of Health
KeywordsFrequentist inferenceBayesian probabilityMedicineClinical trialFrequentist probabilityHarmBayesian statisticsBayes' theoremSkepticismBayesian inferenceIntensive care medicineComputer scienceArtificial intelligencePsychologyEpistemologyPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.136
metaresearch head score (Gemma)0.278
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1360.278
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0130.006
Bibliometrics0.0060.005
Science and technology studies0.0010.004
Scholarly communication0.0090.013
Open science0.0060.003
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0100.002

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 head0.659
GPT teacher head0.625
Teacher spread0.033 · 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 designNot applicable
DomainMethods
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

Citations116
Published2020
Admission routes2
Has abstractno

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