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Record W4405176815 · doi:10.1002/9781683674849.mcm0075

Antimicrobial Susceptibility Testing Systems

2023· other· en· W4405176815 on OpenAlexaff
James A. Karlowsky, Sandra S. Richter, Jean B. Patel

Bibliographic record

VenueClinMicroNow · 2023
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsUniversity of ManitobaShared Health
Fundersnot available
KeywordsAntimicrobialBiologyMicrobiology

Abstract

fetched live from OpenAlex

Abstract This chapter focuses primarily on commercial Antimicrobial Susceptibility Testing (AST) systems currently available in the United States. Only one semiautomated system for disk diffusion testing is marketed in the United States; additional systems are available in other countries. AST systems include data management software that may be interfaced with a laboratory information system and offer various levels of expert system and epidemiological analyses. The manual broth microdilution systems facilitate the visual reading and recording of minimal inhibitory concentrations. The semiautomated broth microdilution systems use automated devices to read susceptibility and identification tests after offline incubation. New technology provided by the Accelerate Pheno system generates AST results directly from positive blood cultures in less time than testing performed on isolates. In the future, the time required to complete AST testing may also be reduced with the application of molecular techniques.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0580.063

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 head0.036
GPT teacher head0.281
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2023
Admission routes1
Has abstractyes

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