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Record W2022246343 · doi:10.1097/shk.0b013e318299f774

Perturbed Mononuclear Phagocyte System in Severely Burned and Septic Patients

2013· review· en· W2022246343 on OpenAlexafffund
Fangming Xiu, Marc G. Jeschke

Bibliographic record

VenueShock · 2013
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsSepsisImmune systemImmunologyMononuclear phagocyte systemMedicineImmunosuppressionAcquired immune systemBurn injurySevere burnInnate immune systemInternal medicine

Abstract

fetched live from OpenAlex

Burn is one of the most common and devastating forms of trauma. Major burn injury disturbs the immune system, resulting in marked alterations in bone marrow hematopoiesis and a progressive suppression of the immune response, which are thought to contribute to increased susceptibility to secondary infections and the development of sepsis. Immunosuppression in patients with severe burn and sepsis leads to high morbidity and mortality in these patients. mononuclear phagocytes system (MPS) is a critical component of the innate immune response and plays key roles in burn immunity. These phagocytes are the first cellular responders to severe burn injury after acute disruption of the skin barrier. They are not only able to internalize and digest bacteria and dead cells and scavenge toxic compounds produced by metabolism, but also able to initiate an adaptive immune response. Severe burn and sepsis profoundly inhibit the functions of dendritic cells, monocytes, and macrophages. Adoptive transfer of MPS or stem cells to patients with severe burn and sepsis that aim to restore MPS function is promising. A better understanding of the roles played by MPS in the pathophysiology of severe burn and sepsis will guarantee a more rational and effective immunotherapy of patients with severe burn and 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2013
Admission routes2
Has abstractyes

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