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Record W2803183027 · doi:10.1159/000487818

The Understanding and Management of Organism Toxicity in Septic Shock

2018· review· en· W2803183027 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Innate Immunity · 2018
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Paul's Hospital
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsSeptic shockImmune systemBiologySepsisInflammasomeImmunologyOrganismInnate immune systemMicrobiologyInflammationGenetics

Abstract

fetched live from OpenAlex

The toxicity caused by different organisms in septic shock is substantially complex and characterized by an intricate pathogenicity that involves several systems and pathways. Immune cells' pattern recognition receptors initiate the host response to pathogens after the recognition of pathogen-associated molecular patterns. In essence, the subsequent activation of downstream pathways may progress to infection resolution or to a dysregulated host response that represents the hallmark of organ injury in septic shock. Likewise, the management of organism toxicity in septic shock is complicated and comprises a multiplicity of suitable targets. In this review, the classic immune responses to pathogens are discussed as well as other factors that are relevant in the pathogenicity of septic shock, including sepsis-induced immune suppression, inflammasome activation, intestinal permeability, and the role of lipids and proprotein convertase subtilisin/kexin type 9. Current therapies aiming to eliminate the organisms causing septic shock, recent and ongoing trials in septic shock treatment, and potential new therapeutic strategies are also explored.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.225
GPT teacher head0.404
Teacher spread0.179 · 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