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Record W2050215441 · doi:10.1086/653116

Getting under the Skin: The Immunopathogenesis of<i>Streptococcus pyogenes</i>Deep Tissue Infections

2010· review· en· W2050215441 on OpenAlexaff
Linda Johansson, Pontus Thulin, Donald E. Low, Anna Norrby‐Teglund

Bibliographic record

VenueClinical Infectious Diseases · 2010
Typereview
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsMount Sinai Hospital
FundersVetenskapsrådetKarolinska InstitutetSvenska Sällskapet för Medicinsk Forskning
KeywordsFasciitisStreptococcus pyogenesMedicineToxic shock syndromeStreptococcusImmunologySTREPTOCOCCAL INFECTIONSDermatologyStaphylococcus aureusSurgeryBiologyBacteria

Abstract

fetched live from OpenAlex

Streptococcus pyogenes can cause a variety of diseases in immunocompetent individuals, from pharyngotonsillitis to life-threatening invasive diseases, such as streptococcal toxic shock syndrome, and rapidly progressing deep-tissue infections, such as necrotizing fasciitis. Necrotizing fasciitis is often seen in combination with streptococcal toxic shock syndrome, which further increases morbidity and mortality. We review here the host-pathogen interactions in the tissue milieu and discuss the use of intravenous immunoglobulin as potential adjunctive therapy in these life-threatening infections.

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)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.056
GPT teacher head0.420
Teacher spread0.364 · 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
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

Citations140
Published2010
Admission routes1
Has abstractyes

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