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Record W3202964273 · doi:10.1007/s00134-021-06506-y

Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021

2021· article· en· W3202964273 on OpenAlexaff
Laura Evans, Andrew Rhodes, Waleed Alhazzani, Massimo Antonelli, Craig M. Coopersmith, Craig French, Flávia Ribeiro Machado, Lauralyn McIntyre, Marlies Ostermann, Hallie C. Prescott, Christa Schorr, Steven Q. Simpson, W. Joost Wiersinga, Fayez Alshamsi, Derek C. Angus, Yaseen M. Arabi, Luciano César Pontes Azevedo, Richard Beale, Gregory J. Beilman, Emilie P. Belley‐Côté, Lisa Burry, Maurizio Cecconi, John Centofanti, Angel Coz Yataco, Jan J. De Waele, R. Phillip Dellinger, Kent Doi, Bin Du, Elisa Estenssoro, Ricard Ferrer, Charles D. Gomersall, Carol Hodgson, Morten Hylander Møller, Theodore J. Iwashyna, Shevin T. Jacob, Ruth Kleinpell, Michael Klompas, Younsuck Koh, Anand Kumar, Arthur Kwizera, Suzana Margareth Lobo, Henry Masur, Steven McGloughlin, Sangeeta Mehta, Yatin Mehta, Mervyn Mer, Mark Nunnally, Simon Oczkowski, Tiffany M. Osborn, Elizabeth Papathanassoglou, Anders Perner, Michael A. Puskarich, Jason A. Roberts, William D. Schweickert, Maureen A. Seckel, Jonathan Sevransky, Charles L. Sprung, Tobias Welte, Janice L. Zimmerman, Mitchell M. Levy

Bibliographic record

VenueIntensive Care Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMount Sinai HospitalUniversity of AlbertaPopulation Health Research InstituteMcMaster UniversityUniversity of ManitobaOttawa HospitalImpact
FundersEuropean Society of Intensive Care MedicineEuropean Society of Clinical Microbiology and Infectious DiseasesIntensive Care SocietyAmerican Thoracic SocietyNational Institute for Health and Care ResearchAmerican Association of Critical-Care NursesSociety for Academic Emergency MedicineEuropean Respiratory Society
KeywordsMedicineSurviving Sepsis CampaignSeptic shockGrading (engineering)Intensive care medicineEvidence-based medicineSepsisMEDLINEAnesthesiologyEvidence-based practiceBest practiceResuscitationAlternative medicineEmergency medicineSevere sepsisSurgeryPathology

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)0.014
metaresearch head score (Gemma)0.031
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: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.031
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.007
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0050.005

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 head0.130
GPT teacher head0.409
Teacher spread0.279 · 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
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

Citations5,021
Published2021
Admission routes1
Has abstractno

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