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Record W2329009047 · doi:10.1177/175114370901000410

The Problems Diagnosing Ventilator-Associated Pneumonia

2009· article· en· W2329009047 on OpenAlexaff
Neil I Stewart, Brian H. Cuthbertson

Bibliographic record

VenueJournal of the Intensive Care Society · 2009
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsVentilator-associated pneumoniaMedicineIntensive care medicinePneumoniaRadiological weaponIntensive care unitHealth careSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Ventilator-associated pneumonia (VAP) is the most common healthcare-associated infection in the intensive care unit. Clinical, radiological and microbiological criteria are used to make the diagnosis, but there is no consensus definition, as no individual criterion or combination of criteria offer sufficient diagnostic accuracy to support their sole use in defining VAP. Neither invasive bronchoscopic sampling nor less invasive quantitative tracheal aspirate, conveys an advantage when making the microbiological diagnosis of VAP. Of the scoring systems and definitions presently in use, the Clinical Pulmonary Infection Score (CPIS) has been shown to be prone to inter-observer variability; the US Centers for Disease Control (CDC) National Healthcare Safety Network (NHSN) definition relies heavily on subjective clinical criteria, and the Hospitals in Europe Link for Infection Control through Surveillance (HELICS) criteria employ similarly subjective clinical criteria with five different possibilities for microbiological diagnosis. The use of these different diagnostic methods leads to marked variation in the reported incidence of VAP. Clinical practice requires an objective and transferable definition for VAP so that we can improve the reporting, monitoring and treatment of VAP.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.287
Teacher spread0.273 · 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 designNot applicable
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

Citations11
Published2009
Admission routes1
Has abstractyes

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