MétaCan
Menu
Back to cohort
Record W4306704105 · doi:10.1128/spectrum.03730-22

A Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry Direct-from-Urine-Specimen Diagnostic for Gram-Negative Pathogens

2022· article· en· W4306704105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsUniversity of Victoria
FundersNational Institute of Allergy and Infectious DiseasesDivision of Intramural Research, National Institute of Allergy and Infectious Diseases
KeywordsMass spectrometryUrineGramTime-of-flight mass spectrometryChromatographyMatrix (chemical analysis)ChemistryAnalytical Chemistry (journal)IonizationMicrobiologyBiologyBacteriaGeneticsIon

Abstract

fetched live from OpenAlex

This study describes and evaluates a direct-from-urine extraction method that allows identification of Gram-negative bacteria via MALDI-TOF MS within 1 h. Currently, identification of urinary pathogens requires 24 h of culture prior to identification. While this method may not replace culture, we demonstrate its utility in screening for common urinary pathogens. By providing identifications in under 1 h, clinicians can potentially treat patients sooner with more-targeted antimicrobial therapy. In turn, earlier treatment can improve patient outcome and antimicrobial stewardship. Furthermore, MADLI-TOF MS is a readily available, easy-to-use diagnostic tool in clinical laboratories, making implementation of this method possible.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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