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Record W2110904468 · doi:10.1086/598185

Clinical Consensus Conference: Survey on Gram‐Positive Bloodstream Infections with a Focus on<i>Staphylococcus aureus</i>

2009· article· en· W2110904468 on OpenAlexaff
Christoph Naber, Larry M. Baddour, Evangelos J. Giamarellos‐Bourboulis, Ian M. Gould, Mathias Herrmann, Bruno Hoen, Adolf W. Karchmer, Yoshio Kobayashi, Roman S. Kozlov, Daniel Lew, José M. Miró, Robert C. Moellering, Philippe Moreillon, Georg Peters, Ethan Rubinstein, Harald Seifert, G. Ralph Corey

Bibliographic record

VenueClinical Infectious Diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsUniversity of Manitoba
FundersNovartis PharmaAstellas PharmaBayer HealthCareGilead SciencesGlaxoSmithKlineUnited Therapeutics CorporationSt. Jude MedicalPfizerBristol-Myers Squibb
KeywordsMedicineIntensive care medicineStaphylococcus aureusEmpirical treatmentIncidence (geometry)EndocarditisBloodstream infectionBacteremiaInfective endocarditisInternal medicineAntibioticsMicrobiologyBacteria

Abstract

fetched live from OpenAlex

The increased incidence over the past decade of bloodstream infections (BSIs) caused by gram-positive bacteria, particularly methicillin-resistant Staphylococcus aureus, highlights the critical need for a consistent approach to therapy. However, there is currently no international consensus on the diagnosis and management of gram-positive BSIs. The Clinical Consensus Conference on Gram-Positive Bloodstream Infections was convened as a session at the 9th International Symposium on Modern Concepts in Endocarditis and Cardiovascular Infections held in 2007. Participants discussed various aspects of the practical treatment of patients who present with gram-positive BSI, including therapeutic options for patients with BSIs of undefined origin, the selection of appropriate empirical therapy, and treatment of complicated and uncomplicated BSIs. The opinions of participants about these key issues are reflected in this article.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.381
Teacher spread0.334 · 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 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

Citations50
Published2009
Admission routes1
Has abstractyes

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