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Record W3046778617 · doi:10.1097/pcc.0000000000002553

Perspective of the Surviving Sepsis Campaign on the Management of Pediatric Sepsis in the Era of Coronavirus Disease 2019*

2020· article· en· W3046778617 on OpenAlexaff
Scott L. Weiss, Mark Peters, Michael S. D. Agus, Waleed Alhazzani, Karen Choong, Heidi R. Flori, David Inwald, Simon Nadel, Mark Nunnally, Luregn J. Schlapbach, Robert C. Tasker, Pierre Tissières, Niranjan Kissoon

Bibliographic record

VenuePediatric Critical Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsBC Children's HospitalMcMaster University
FundersNational Institute of General Medical Sciences
KeywordsMedicineSepsisSeptic shockIntensive care medicineOrgan dysfunctionDiseaseResuscitationShock (circulatory)CoronavirusRespiratory failureCoronavirus disease 2019 (COVID-19)ImmunologyInternal medicineInfectious disease (medical specialty)Emergency medicine

Abstract

fetched live from OpenAlex

Severe acute respiratory syndrome coronavirus 2 is a novel cause of organ dysfunction in children, presenting as either coronavirus disease 2019 with sepsis and/or respiratory failure or a hyperinflammatory shock syndrome. Clinicians must now consider these diagnoses when evaluating children for septic shock and sepsis-associated organ dysfunction. The Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-associated Organ Dysfunction in Children provide an appropriate framework for the early recognition and initial resuscitation of children with sepsis or septic shock caused by all pathogens, including severe acute respiratory syndrome coronavirus 2. However, the potential benefits of select adjunctive therapies may differ from non-coronavirus disease 2019 sepsis.

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)0.010
metaresearch head score (Gemma)0.016
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0190.023
Insufficient payload (model declined to judge)0.0140.004

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 head0.087
GPT teacher head0.377
Teacher spread0.290 · 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
GenreCommentary

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

Citations22
Published2020
Admission routes1
Has abstractyes

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