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Agar Dilution MIC Test

2023· other· en· W4406365305 on OpenAlexaff
Samir Patel

Bibliographic record

VenueClinMicroNow · 2023
Typeother
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsAgar dilutionDilutionAgar Dilution MethodTest (biology)AgarBiologyMicrobiologyPhysicsMinimum inhibitory concentrationGeneticsBacteriaBotany

Abstract

fetched live from OpenAlex

Abstract The agar dilution MIC test is a quantitative method for the determination of in vitro activity of an antimicrobial agent against bacterial isolates, such as aerobic and anaerobic bacteria. Following incubation using conditions appropriate for the test organisms, the MIC is determined by observing the lowest concentration of antimicrobial agent that inhibits visible growth of each test isolate. MICs obtained are interpreted as susceptible, intermediate, susceptible dose‐dependent, resistant, or non‐susceptible based on criteria defined by the CLSI or by the European Committee on Antimicrobial Susceptibility Testing. Recommendations for performance of the agar dilution method for susceptibility testing of aerobic bacteria are found in the CLSI M07 standard and include procedures for both non‐fastidious and fastidious organisms. The chapter covers proper quality control principles for analytical tests. With postanalytical considerations, it provides attention to common test limitations, interpretive challenges, and results reporting.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.010

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 head0.044
GPT teacher head0.380
Teacher spread0.337 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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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