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To qualitatively aid in the identification by light microscopy of human cells of lymphoid origin, by recognizing lambda light chains in immunoqlobulin secreting plasma cells and plasmacytoid B lymphocytes in normal and pathologic paraffin embedded tissues processed in neutral buffered formalin, 85, or Bouin's fixative. Positive results aid in the differential diagnosis, classification, and immunophenotyping of lymphomas and must be interpreted by a pathologist within the context of clinical data, gross and microscopic morphological criteria and multiple chemical and immunohistochemical stains.

ChemMate™ Lambda is comprised of a rabbit polyclonal antibody to human lambda light chains. The antibody reacts with free lambda light chains as well as lambda chains in intact immunoglobulin molecules.

The provided 510(k) summary for ChemMate™ Lambda (K973392) describes an immunohistochemical reagent designed to qualitatively identify human lambda light chains in tissue samples. This is a reagent used in a laboratory setting, not a device with algorithmic performance and acceptance criteria in the typical sense of AI/ML or image processing devices.

Therefore, the requested information regarding acceptance criteria, study design for device performance (including sample size, ground truth, expert opinions, MRMC studies, standalone performance), and training set details, is not applicable to this type of product submission. The summary focuses on the reagent's intended use, its immunoreactivity, and its reproducibility as a laboratory tool, rather than on a diagnostic algorithm's performance metrics.

Instead, the submission provides evidence of the reagent's utility through:

1. Immunoreactivity based on established scientific literature:

• The document heavily cites various published studies (Picker et al., Hitzman et al., Petruch et al., Harris et al., Mori et al., Meis et al., Rabia and Kahn, Popperna et al., Kadin et al., Cleary et al., Ernst et al.) to establish the expected reactivity of lambda and kappa light chains in normal and pathological tissues.
• These references describe cases where lambda light chains are expressed in B-cell neoplasms (often monotypically) and in normal/reactive lymphoid tissues (often polytypically with kappa). They also highlight limitations and areas where caution is needed in interpretation (e.g., in Hodgkin's disease, passive diffusion, non-lymphoid tissues).
• This literature serves as the primary "proof" of the reagent's ability to identify lambda light chains as intended.

2. Reproducibility Study:

• Acceptance Criteria (Implicit for a reagent): Consistent staining results. While no quantitative metrics are provided, the stated goal is "Consistent staining results were obtained."
• Reported Device Performance: "Consistent staining results were obtained."
• Sample Size for Test Set: 281 tissue specimens (normal and tumor specimens).
• Data Provenance: Not explicitly stated, but typical for such studies would be archived clinical samples from a pathology lab. It's retrospective as these are "serial sections of... tissue specimens."
• Number of Experts and Qualifications: Not specified, but generally, a pathologist would interpret such staining results.
• Adjudication Method: Not specified.
• MRMC Comparative Effectiveness Study: Not applicable (this is for assessing human reader performance with/without AI assistance).
• Standalone Performance: The reproducibility study evaluates the reagent's performance on its own, independent of a specific human interpretation strategy, but it's not "standalone algorithm performance" in the AI sense.
• Type of Ground Truth: The "ground truth" for reproducibility is the expectation of staining patterns in known tissue types (normal vs. tumor, specific pathologies), based on established histopathological diagnoses.
• Sample Size for Training Set: Not applicable; there is no AI/ML model to train. The reagent formulation and optimization are based on chemical and biological principles.
• How Ground Truth for Training Set was Established: Not applicable.

In summary, this 510(k) submission for ChemMate™ Lambda relies on literature review and a reproducibility study to demonstrate its effectiveness as a qualitative immunohistochemical reagent. It does not involve acceptance criteria or study designs typically associated with AI-powered diagnostic devices.

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