MétaCan
Menu
Back to cohort
Record W3005429114 · doi:10.1007/s00134-019-05878-6

Surviving sepsis campaign international guidelines for the management of septic shock and sepsis-associated organ dysfunction in children

2020· article· en· W3005429114 on OpenAlexaff
Scott L. Weiss, Mark Peters, Waleed Alhazzani, Michael S. D. Agus, Heidi R. Flori, David Inwald, Simon Nadel, Luregn J. Schlapbach, Robert C. Tasker, Andrew C. Argent, Joe Brierley, Joseph A. Carcillo, Enitan D. Carrol, Christopher L. Carroll, Ira M. Cheifetz, Karen Choong, Jeffry J. Cies, Andrea T. Cruz, Danièle De Luca, Akash Deep, Saul N. Faust, Cláudio Flauzino de Oliveira, Mark W. Hall, Paul Ishimine, Étienne Javouhey, Koen Joosten, Poonam Joshi, Oliver Karam, Martin C. J. Kneyber, Joris Lemson, Graeme MacLaren, Nilesh M. Mehta, Morten Hylander Møller, Christopher J. L. Newth, Trung Nguyen, Akira Nishisaki, Mark Nunnally, Margaret M. Parker, Raina Paul, Adrienne G. Randolph, Suchitra Ranjit, Lewis H. Romer, Halden F. Scott, Lyvonne N. Tume, Judy Verger, Eric A. Williams, Joshua Wolf, Hector R. Wong, Jerry J. Zimmerman, Niranjan Kissoon, Pierre Tissières

Bibliographic record

VenueIntensive Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsImpactBC Children's HospitalMcMaster University
FundersEuropean Society of Intensive Care MedicineIntensive Care SocietyNational Institute for Health and Care ResearchAmerican Association of Critical-Care NursesAmerican Thoracic Society
KeywordsMedicineSeptic shockAnesthesiologySepsisSurviving Sepsis CampaignPain medicineOrgan dysfunctionIntensive care medicineShock (circulatory)Severe sepsisEmergency medicineInternal medicineAnesthesia

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0030.002

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 head0.093
GPT teacher head0.356
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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".

Quick stats

Citations592
Published2020
Admission routes1
Has abstractno

Explore more

Same venueIntensive Care MedicineSame topicSepsis Diagnosis and TreatmentFrench-language works237,207