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Record W1989225449 · doi:10.1021/pr9006427

Pneumococcal Pneumonia: Potential for Diagnosis through a Urinary Metabolic Profile

2009· article· en· W1989225449 on OpenAlexaff
Carolyn M. Slupsky, Kathryn N. Rankin, Hao Fu, David Chang, Brian H. Rowe, Patrick G. P. Charles, Allison McGeer, Donald E. Low, Richard Long, Dennis Kunimoto, Michael B. Sawyer, Richard N. Fedorak, Darryl J. Adamko, Erik J. Saude, Sirish L. Shah, Thomas J. Marrie

Bibliographic record

VenueJournal of Proteome Research · 2009
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of TorontoUniversity of AlbertaMount Sinai Hospital
Fundersnot available
KeywordsPneumoniaStreptococcus pneumoniaeCommunity-acquired pneumoniaUrinary systemMicrobiologyUrinePathogenAntimicrobialBacterial pneumoniaPneumococcal pneumoniaPathogenic bacteriaRespiratory tractMetabolomicsRespiratory tract infectionsBiologyImmunologyBacteriaMedicineRespiratory systemInternal medicineAntibioticsBioinformatics

Abstract

fetched live from OpenAlex

Pneumonia, an infection of the lower respiratory tract, is caused by any of a number of different microbial organisms including bacteria, viruses, fungi, and parasites. Community-acquired pneumonia (CAP) causes a significant number of deaths worldwide, and is the sixth leading cause of death in the United States. However, the pathogen(s) responsible for CAP can be difficult to identify, often leading to delays in appropriate antimicrobial therapies. In the present study, we use nuclear magnetic resonance spectroscopy to quantitatively measure the profile of metabolites excreted in the urine of patients with pneumonia caused by Streptococcus pneumoniae and other microbes. We found that the urinary metabolomic profile for pneumococcal pneumonia was significantly different from the profiles for viral and other bacterial forms of pneumonia. These data demonstrate that urinary metabolomic profiles may be useful for the effective diagnosis of CAP.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.083
GPT teacher head0.425
Teacher spread0.342 · 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 designBench or experimental
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

Citations98
Published2009
Admission routes1
Has abstractyes

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