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Record W2914059975 · doi:10.3389/fendo.2019.00071

Influence of Obesity on Pneumococcus Infection Risk in the Elderly

2019· review· en· W2914059975 on OpenAlexafffund
Daniela Frasca, Janet E. McElhaney

Bibliographic record

VenueFrontiers in Endocrinology · 2019
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsHealth Sciences North
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on AgingNational Institutes of HealthCanadian Immunization Research NetworkNorthern Ontario Heritage Fund Corporation
KeywordsMedicineObesityInfection riskRisk of infectionRisk factorIntensive care medicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Obesity negatively affects immune function and host defense mechanisms. Obesity is associated with chronic activation of the innate immune system and consequent local and systemic inflammation which contribute to pathologic conditions such as type-2 diabetes mellitus, cancer, psoriasis, atherosclerosis and inflammatory bowel disease. Individuals with obesity have increased susceptibility to contract viral, bacterial and fungal infections and respond sub-optimally to vaccination. In this review, we summarize research findings on the effects of obesity on immune responses to respiratory tract infections (RTI), focusing on Streptococcus pneumoniae (“pneumococcus”) infection, which is a major cause of morbidity and mortality in the US, causing community-acquired infections such as pneumonia, otitis media and meningitis. We show that the risk of infection is higher in elderly individuals and also in individuals of certain ethnic groups, although in a few reports obesity has been associated with better survival of individuals admitted to hospital with pneumococcus infection, a phenomenon known as “obesity paradox”. We discuss factors that are associated with increased risk of pneumococcal infection, such as recent infection with RTI, chronic medical conditions and immunosuppressive medications.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.056
GPT teacher head0.385
Teacher spread0.329 · 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

Citations57
Published2019
Admission routes2
Has abstractyes

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