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Record W2120233207 · doi:10.1086/322021

Microbiological and Inflammatory Factors Associated with the Development of Pneumococcal Pneumonia

2001· article· en· W2120233207 on OpenAlexaff
Frédéric Dallaire, Nathalie Ouellet, Yves Bergeron, Véronique Turmel, Marie‐Christine Gauthier, Marie Simard, Michel G. Bergeron

Bibliographic record

VenueThe Journal of Infectious Diseases · 2001
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStreptococcus pneumoniaePneumoniaPneumococcal pneumoniaImmunologyTumor necrosis factor alphaInflammationMacrophage inflammatory proteinLungBiologyPathogenesisMicrobiologyImmune systemSepsisBacterial pneumoniaMedicineChemokineAntibioticsInternal medicine

Abstract

fetched live from OpenAlex

Pneumococcal pneumonia still is associated with a high mortality rate, despite appropriate antimicrobial therapy. Many gaps remain in the understanding of the pathogenesis of this deadly infection. The microbial and inflammatory events that characterize survival or death after intranasal inoculation of mice with an LD(50) inoculum of Streptococcus pneumoniae were investigated. Survival was associated with rapid bacterial clearance and low inflammation (surfactant and red blood cells in alveoli), but no neutrophil recruitment or lung tissue injury was noted. By contrast, death was preceded by strong bacterial growth that peaked 48 h after the infection and was associated with gradual increases in pulmonary levels of interleukin-6, macrophage inflammatory protein (MIP)-1alpha, MIP-2, monocyte chemoattractant protein-1, KC, and neutrophil recruitment. The injection of tumor necrosis factor-alpha or the addition of lipopolysaccharide or heat-killed S. pneumoniae to the inoculum enhanced early host response and survival. These observations may help develop appropriate markers of evolution of pneumonia, as well as new therapeutic strategies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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.

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

Citations116
Published2001
Admission routes1
Has abstractyes

Explore more

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