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Safety Vest is intended for patients who require restraint while in the wheelchair or bed.

Economy Safety Vest is intended for patients who require restraint while in the wheelchair or bed.

Vest with Shoulder Straps is intended for patients who require restraint and posture support while in the wheelchair.

Safety Vest: A vest-style, upper body device made from either woven polyester or polyester mesh. A 1 1'8-inch-wide polyester strap attached to one side of the vest at the waist is threaded through a slot on the other causing the vest to criss-cross and close in front of the patient. The waist strap secures to the kickspurs of the wheelchair or to the bed frame. The edges of the garment are finished with a bias cut binding, the color of which corresponds to a size chart included on the package insert. Device is intended for wheelchair or bed use.

Economy Safety Vest: Identical to above except that it is made from a lighter weight woven polyester material and is not available in polvester mesh. Unlike the above device which is intended for multiple uses, this is a singe-patient-use device. This fact is noted on the garment label along with a notation that it is not to be laundered.

Vest with Shoulder Straps: Identical with Safety Vest with the addition of 1 1/8-inchwide webbing straps sewn to the shoulder area. These straps are secured to the pushhandles to provide patient posture support. This device is for wheelchair use only.

The provided text describes a 510(k) summary for Skil-Care Corporation's Safety Vests. This document is a premarket notification to the FDA for a medical device and is primarily focused on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria through a clinical study.

Therefore, many of the requested categories for a study that "proves the device meets the acceptance criteria" are not applicable in this context. The document explicitly states: "These devices are currently exempt from 510(k) Premarket Notification Procedures and Good Manufacturing Practice Regulations and are legally marketed by Skil-Care Corporation as of the date of this submission." This indicates that the submission is about changing labeling to align with FDA guidance, rather than demonstrating new performance or safety from scratch.

Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is not applicable:

1. Table of acceptance criteria and the reported device performance

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

• Not applicable. This document does not describe a clinical test set with a sample size. It refers to already marketed devices.

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

• Not applicable. There is no described test set requiring ground truth established by experts.

4. Adjudication method for the test set

• Not applicable. There is no described test set requiring an adjudication method.

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 physical restraint vest, not an AI-powered diagnostic or assistive tool. MRMC studies are irrelevant here.

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

• Not applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Not applicable. There is no described study requiring ground truth. The "ground truth" for the submission is the long-standing market history and accepted use of similar predicate devices, and the alignment with FDA's draft "Guidance on the Content of Premarket Notification [510(k)] Submissions for Protective Restraints."

8. The sample size for the training set

• Not applicable. There is no algorithm or training set involved.

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

• Not applicable. There is no algorithm or training set involved.

In summary: This document is a 510(k) summary focused on demonstrating substantial equivalence and compliance with labeling guidance for existing medical devices (safety vests) that were previously exempt from 510(k) notification. It does not present a new study with acceptance criteria, test sets, or performance data in the way one would for a novel device undergoing clinical trials. The "proof" of the device's acceptability relies on its long history of marketing and commercial distribution, and its substantial equivalence to previously cleared (or exempt) products.

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The Johnson & Johnson Safety Vests are designed to protect patients that are at risk of self injury or interruption of medical care by restraining them . These products can be used in restraining patients to a bed or chair.
The Johnson & Johnson Limb Holders are designed to protect patients that are at risk of self injury or interruption of medical care by restraining them . These products can be used in restraining patients to a bed or chair.

The Johnson & Johnson Safety Vests are constructed of lightweight nylon mesh with webbed tie straps and shoulder loops. These vests may be attached to either a chair or a bed.
The Johnson & Johnson Canvas Limb Holder is constructed of a cotton canvas wrist protector with a soft cotton lining, and a polyester strap. The Economy Limb Holder is constructed of a nylon laminated foam with hook and loop closure and polyester straps.

Here's an analysis of the provided text regarding the "Johnson & Johnson Safety Vests" based on your request. It's important to note that the provided text is a "Summary of Safety & Effectiveness Data" for medical devices, and as such, it does not contain the kind of detailed information about AI algorithm development, acceptance criteria studies, or ground truth establishment that would be present for an AI/ML medical device.

The document describes a traditional medical device (physical restraint vest), not an AI-powered diagnostic or predictive tool. Therefore, many of your requested points regarding AI/ML studies are not applicable.

However, I will extract the available information and highlight what is missing based on your template.

Device Name: Johnson & Johnson Safety Vests

1. Table of acceptance criteria and the reported device performance

Study that proves the device meets the acceptance criteria:

The document states: "The safety and effectiveness of the Johnson & Johnson Safety Vest and the Safety Vest Poncho Style is substantiated by its similarity in design, characteristics, and indications for use, to the currently marketed Posey Waist & Chest Vest and the Posey Criss-Cross Vest, by J.T. Posey Company."

This indicates that no primary clinical trial or performance study was conducted for this specific device to establish its safety and effectiveness against explicit performance criteria. Instead, the device's acceptance is based on demonstrating substantial equivalence to existing, legally marketed predicate devices. This is a common regulatory pathway for Class I and II medical devices in the US (510(k) pathway), where new devices do not necessarily require de novo studies if they can prove to be "as safe and effective" as a predicate.

The following information is NOT APPLICABLE or NOT PRESENT in the provided document, as it pertains to AI/ML device studies, not traditional medical device regulatory submissions based on substantial equivalence:

2. Sample size used for the test set and the data provenance: Not applicable (no test set in the AI/ML sense).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable (no ground truth in the AI/ML sense for this type of device).

4. Adjudication method (e.g., 2+1, 3+1, none) 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 (no AI component, no human readers involved in diagnostic interpretation).

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable (no ground truth in the AI/ML sense). The "truth" of its safety and effectiveness is inferred from the established safety and effectiveness of the predicate devices.

8. The sample size for the training set: Not applicable (no training set for an AI algorithm).

9. How the ground truth for the training set was established: Not applicable.

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