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Record W4387949418 · doi:10.1128/jcm.00606-23

Use of genome sequencing to resolve differences in gradient diffusion and agar dilution antimicrobial susceptibility testing performance of <i>Neisseria gonorrhoeae</i> isolates in Alberta, Canada

2023· article· en· W4387949418 on OpenAlexafffundabout
Angela Ma, Christina Ferrato, Irene Martín, Petra Smyczek, Jennifer Gratrix, Tanis C. Dingle

Bibliographic record

VenueJournal of Clinical Microbiology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsAlberta Health ServicesPublic Health Agency of CanadaProvincial Laboratory of Public HealthUniversity of CalgaryUniversity of Alberta
FundersAlberta Precision Laboratories
KeywordsCefiximeAgar dilutionNeisseria gonorrhoeaeAzithromycinTetracyclinePenicillinCiprofloxacinMicrobiologyBiologyCeftriaxoneMinimum inhibitory concentrationAntimicrobialAntibiotics

Abstract

fetched live from OpenAlex

ABSTRACT Agar dilution is the gold standard method for phenotypic antimicrobial susceptibility testing (AST) for Neisseria gonorrhoeae . However, this method is laborious and requires expertise, so laboratories that perform N. gonorrhoeae AST may choose alternative methods such as disk diffusion and gradient diffusion. In this study, we retrospectively compare the performance of gradient diffusion to agar dilution for 2,394 unique N. gonorrhoeae isolates identified in Alberta from 2017 to 2020 against azithromycin, cefixime, ceftriaxone, ciprofloxacin, penicillin, and tetracycline. Genome sequencing was utilized to resolve discrepancies between AST methods, detect antimicrobial resistance markers, and identify trends between error rates and sequence types (STs) of isolates. Over 90% of N. gonorrhoeae isolates were susceptible to azithromycin, cefixime, and ceftriaxone, whereas decreased susceptibility was observed for ciprofloxacin, penicillin, and tetracycline. Categorical (CA) and essential agreement (EA) was poorest between the two methods for penicillin (CA: 86.02%; EA: 77.69%) and tetracycline (CA: 47.22%; EA: 55.96%); however, the low CA was primarily attributed to minor errors. Antimicrobial agents with errors outside of acceptable limits included azithromycin (very major error: 18.42%; major error: 7.73%) and tetracycline (very major error: 6.17%). Genome sequencing on a subset of isolates resolved 30.3% of the azithromycin major errors and confirmed the azithromycin or tetracycline very major errors. Significant associations between certain STs and error types for azithromycin and tetracycline were also identified. Overall, gradient diffusion compared well to agar dilution for cefixime, ceftriaxone, and ciprofloxacin, and genome sequencing was identified as a useful tool to arbitrate discrepant susceptibility testing results between gradient diffusion and agar dilution for N. gonorrhoeae .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.086
GPT teacher head0.323
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes3
Has abstractyes

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