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Record W2906691128 · doi:10.1080/17476348.2019.1562339

The burden of community-acquired bacterial pneumonia in the era of antibiotic resistance

2018· review· en· W2906691128 on OpenAlexaff
Paula Peyrani, Lionel A. Mandell, Antoní Torres, Glenn S. Tillotson

Bibliographic record

VenueExpert Review of Respiratory Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAntibiotic resistanceIntensive care medicineStreptococcus pneumoniaePneumoniaIncidence (geometry)Community-acquired pneumoniaDisease burdenPopulationBurden of diseaseDiseaseAntibioticsBacterial pneumoniaEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Community-acquired pneumonia (CAP) is a significant global health problem and leading cause of death and hospitalization in both the US and abroad. Increasing macrolide resistance among Streptococcus pneumoniae and other pathogens results in a greater disease burden, along with changing demographics and a higher preponderance of comorbid conditions. Areas covered: This review summarizes current data on the clinical and economic burden of CAP, with particular focus on community-acquired bacterial pneumonia (CABP). Incidence, morbidity and mortality, and healthcare costs for the US and other regions of the world are among the topics covered. Major factors that are believed to be contributing to the increased impact of CABP, including antimicrobial resistance, the aging population, and the incidence of comorbidities are discussed, as well as unmet needs in current CABP management. Expert commentary: The clinical and economic burden of CABP is staggering, far-reaching, and expected to increase in the future as new antibiotic resistance mechanisms emerge and the world's population ages. Important measures must be initiated to stabilize and potentially decrease this burden. Urgent needs in CABP management include the development of new antimicrobials, adjuvant therapies, and rapid diagnostics.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.396
Teacher spread0.330 · 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 designSystematic review
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

Citations146
Published2018
Admission routes1
Has abstractyes

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