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The Liofilchem® MIC Test Strip (MTS) is a quantitative method intended for the in vitro determination of antimicrobial susceptibility of bacteria. MTS consists of specialized paper impregnated with a pre-defined concentration gradient of an antimicrobial agent, which is used to determine the minimum inhibitory concentration (MIC) in ug/mL of antimicrobial agents against bacteria as tested on agar media using overnight incubation and manual reading procedures.

The Clindamycin MTS at concentrations of 0.016-256 ug/mL should be interpreted at 16-20 hours of incubation.

The non-fastidious bacteria that have been shown to be active both clinically and in vitro against Clindamycin according to the FDA label are:

Staphylococcus aureus (methicillin-susceptible strains)

MTS consists of specialized paper impregnated with a pre-defined concentration gradient of an antimicrobial agent.

I am sorry, but the provided text describes an FDA 510(k) premarket notification for an antimicrobial susceptibility test strip (Liofilchem MIC Test Strip, Clindamycin). This document is a regulatory approval letter and does not contain the detailed study information needed to answer your questions about acceptance criteria and device performance in the context of an AI/machine learning study.

The document discusses:

• The device: Liofilchem MIC Test Strip (MTS), Clindamycin 0.016 - 256μg/mL
• Its purpose: In vitro determination of antimicrobial susceptibility of bacteria.
• Regulation and product codes.
• General controls provisions for marketed devices.
• Indications for Use: Specifically for Staphylococcus aureus (methicillin-susceptible strains) against Clindamycin, with incubation and manual reading procedures.

It does not include any information about:

• A table of acceptance criteria or reported device performance for an AI/ML system.
• Sample sizes for test or training sets related to an AI/ML model.
• Data provenance (e.g., country of origin, retrospective/prospective).
• Number or qualifications of experts used to establish ground truth for AI/ML.
• Adjudication methods for AI/ML test sets.
• Multi-reader multi-case (MRMC) comparative effectiveness studies with AI assistance.
• Standalone algorithm performance.
• Type of ground truth (e.g., pathology, outcomes data) for an AI/ML study.
• How ground truth was established for training sets in an AI/ML context.

Therefore, I cannot provide an answer that meets your requirements based on the given input.

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