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Jobst Travel Socks are indicated for the following conditions: Help prevent edema and leg discomfort and help prevent deep vein thrombosis for long distance travelers.

Both products are manufactured on circular knit machines. Both products are made of nylon and spandex yarns and provide compression at the ankle of approximately 15-20mmHg. However, the JOBST Travel Socks also have cotton varns for added comfort. While both products are sized based on calf circumference, sizing information based on ankle circumference and shoe size will also be provided with the JOBST Travel Socks, for the convenience of the traveler. Compression is provided for these products by elastic yarns that act circumferentially on the limb. The gradient compression present in these products helps reduce capillary leakage, prevent pooling of blood and improve blood flow. Consequently, they act prophylactically to help reduce the risk of developing deep vein thromboses (DVT) and superficial thromboembolisms.

This 510(k) summary (K032325) for the Jobst Travel Socks primarily establishes substantial equivalence to a predicate device, the Jobst Anti-Em/GP stocking (K955138), rather than presenting a detailed study that proves the device meets specific performance acceptance criteria. Therefore, most of the requested information regarding acceptance criteria and a study design will not be directly derivable from this document.

The core argument for equivalence is based on:

• Materials: Both use nylon and spandex yarns, and Jobst Travel Socks also include cotton.
• Mode of Action: Both provide compression to reduce capillary leakage, prevent blood pooling, and improve blood flow, thereby prophylactically reducing DVT and superficial thromboembolism risk.
• Indications for Use: Both are indicated for individuals subjected to long periods of immobility, with the Travel Socks specifically mentioning "long distance travelers" and helping to "prevent edema and leg discomfort."
• Compression Level: Both provide approximately 15-20mmHg compression at the ankle.

Here's a breakdown of the requested information based on the provided text:

1. Table of acceptance criteria and the reported device performance

Since this is a substantial equivalence submission, specific numerical acceptance criteria for a new clinical study are not provided. The "performance" is implicitly tied to being "substantially equivalent" to the predicate device in terms of materials, mode of action, compression, and intended use.

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not applicable. No new clinical test set is described or referenced in this 510(k) summary for the purpose of demonstrating performance against acceptance criteria. The submission relies on the established safety and effectiveness of the predicate device.
• Data Provenance: Not applicable.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. No new test set requiring expert ground truth establishment is described.

4. Adjudication method for the test set

• Not applicable.

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

• Not applicable. This device is a medical support stocking, not an AI-powered diagnostic or assistive technology for human readers.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

• Not applicable. This device is a physical medical support stocking.

7. The type of ground truth used

• Not applicable in the context of a new performance study. The "ground truth" for this 510(k) is the established safety and effectiveness of the predicate device (Jobst Anti-Em/GP stocking, K955138), which presumably underwent its own evaluation or was cleared based on prior substantial equivalence arguments.

8. The sample size for the training set

• Not applicable. This device does not involve a training set as it is not a machine learning model.

9. How the ground truth for the training set was established

• Not applicable.

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