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Record W1992319434 · doi:10.1186/cc14006

High frequency of myeloid-derived suppressor cells in sepsis patients, with the granulocytic subtype dominating in Gram-positive cases

2014· article· en· W1992319434 on OpenAlexfundno aff
Helena Janols, Caroline Bergenfelz, Roni Allaoui, Anna-Maria Larsson, Lisa Rydén, Sven Björnsson, Sabina Janciauskiene, Marlene Wullt, Anders Bredberg, Karin Leandersson

Bibliographic record

VenueCritical Care · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
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
KeywordsMyeloid-derived Suppressor CellMyeloid cellsMedicineSepsisSuppressorImmunologyInflammationMyeloidImmune systemCancerPopulationAutoimmunityCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Myeloid-derived suppressor cells (MDSCs) constitute a heterogeneous population of immature myeloid cells that potently suppress immune responses. They were originally identified in cancer patients and have since been reported to occur also in chronic inflammation, autoimmunity and even bacterial infections. Human MDSCs are commonly divided into monocytic (Mo-MDSCs) and granulocytic (PMN-MDSCs) subtypes. To what extent the bona fide cancer MDSCs are representative of the proposed MDSCs found in other diseases is not well known. PMN-MDSCs have previously been found to be enriched among low-density granulocytes (LDGs) in density gradient centrifuged blood.

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)0.000
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.232
Teacher spread0.226 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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