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Record W2056326279 · doi:10.1097/mcp.0000000000000052

Community-acquired pneumonia

2014· review· en· W2056326279 on OpenAlexaff
Leah Remington, Wendy Sligl

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of AlbertaUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicinePneumoniaCommunity-acquired pneumoniaIntensive care medicineMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review examines the epidemiology, diagnosis, prognosis, treatment and prevention of community-acquired pneumonia (CAP) in adults. RECENT FINDINGS: CAP is a significant cause of morbidity and mortality. Streptococcus pneumoniae is the most common CAP pathogen; however, microbial cause varies by geographic location and host factors. Identification of a microbial cause in CAP remains challenging - 30-65% of cases do not have a pathogen isolated. The use of molecular techniques in addition to culture, serology and urinary antigen testing has improved diagnostic yield. Scoring systems are useful for CAP prognostication and site of care decisions. Studies evaluating novel biomarkers including pro-B-type natriuretic peptide and procalcitonin suggest potential adjunctive roles in CAP prognosis. Guideline-based treatment for CAP has changed little in recent years. Effective and timely antimicrobial therapy is crucial in optimizing outcomes and should be based on local antimicrobial susceptibility patterns. Macrolides may have additional anti-inflammatory properties and a mortality benefit in severe CAP. Preventive strategies include immunization and modification of specific patient risk factors. SUMMARY: CAP is common and causes considerable morbidity and mortality. A comprehensive approach including advanced diagnostic testing, effective and timely antimicrobial therapy and prevention is required to optimize CAP outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.195
GPT teacher head0.454
Teacher spread0.259 · 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 designOther design
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

Citations44
Published2014
Admission routes1
Has abstractyes

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