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For post-operative drainage of serosanguineous fluids and blood away from operative site; for use with suction reservoir as a closed wound drain system

Two joined silicone tubing sections; flat drain section is partially perforated for fluid collection, is ribbed to prevent collapse as suction is applied, and has blue radiopaque stripe; round tubing is connected to suction reservoir inlet; offered with an introduction Trocar attached as an option; silver ion surface treated to ease insertion and removal.

This 510(k) summary describes a medical device, not an AI/ML algorithm. Therefore, many of the requested categories in your prompt are not applicable. I will address the relevant sections based on the provided text and indicate "N/A" for those that do not apply to a device submission of this nature.

Here's the analysis of the provided information:

Acceptance Criteria and Device Performance

The 510(k) summary does not explicitly list "acceptance criteria" in the format typically used for AI/ML performance metrics (e.g., sensitivity, specificity, AUC). Instead, for device submissions, the acceptance criteria are generally met by demonstrating substantial equivalence to a predicate device through shared technological characteristics, intended use, and performance claims.

The device performance is primarily established by referencing the performance of the predicate devices. The summary states:

"Sil-Med Corporation's Flat Suction Drain/Kit with Spi-Argent™ II, (with and without Trocar) is the same drain as the Sil-Slip Flat Suction Drain/Kit (with and without Trocar), K926408. The surface treatment is the same as the surface treatment on Sil-Med Corporation's Mediastinal Silicone Drain, K942709. No further claims will be made for the Flat Suction Drain/Kit with Spi-Argent™ II than made on K942709."

This indicates that the new device is considered to perform equivalently to the predicate devices.

Study-Specific Information (Not applicable for this type of submission)

The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device, not on presenting novel study results for AI/ML performance. Therefore, the following sections are not applicable and the information is not present in the provided text.

2. Sample size used for the test set and the data provenance: N/A (No test set for AI/ML performance is described).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: N/A (No expert ground truth for AI/ML performance is described).
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: N/A (No test set for AI/ML performance is described).
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: N/A (This is a physical medical device, not an AI/ML system).
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: N/A (This is a physical medical device, not an AI/ML system).
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): N/A (No ground truth, in the context of AI/ML, is mentioned).
8. The sample size for the training set: N/A (This is a physical medical device, not an AI/ML system).
9. How the ground truth for the training set was established: N/A (This is a physical medical device, not an AI/ML system).

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