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Record W1980919939 · doi:10.1186/cc14016

Proinflammatory versus anti-inflammatory response in sepsis patients: looking at the cytokines

2014· article· en· W1980919939 on OpenAlexfundno aff
D Anand, Saumitra Ray, Seema Bhargava, Sucharita Das, Ashish Garg, S. R. Taneja, Debashish Dhar, L. M. Srivastava

Bibliographic record

VenueCritical Care · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
FundersChildren's Health FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaFinanciadora de Estudos e ProjetosCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorJapan Society for the Promotion of ScienceRussian Foundation for Basic ResearchLondon Health Sciences Centre
KeywordsProinflammatory cytokineSepsisMedicineTimelineInflammationImmunologyImmune systemMortality rateInternal medicine

Abstract

fetched live from OpenAlex

Despite improvements in supportive care, mortality rates in sepsis remain substantially high. Sepsis exhibits phases of enhanced inflammation, alternating with immune suppression with a resultant variant time point of mortality; yet no human study has correlated levels of cytokines to the timeline of mortality. Our study attempts to analyze the association of levels of proinflammatory and anti-inflammatory cytokines in sepsis with the timeline of death in terms of early (<5 days) versus late (>5 days) mortality, and day of death. We also assessed correlation of these cytokines with length of stay.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.336
Teacher spread0.297 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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