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The GRIPPER PLUS™ Needle is indicated for the administration into or withdrawal of fluids from implanted ports. It is designed to help protect against exposure to bloodborne pathogens caused by accidental needlestick injuries.

The GRIPPER PLUS™ Needle is similar in design to the current GRIPPER® Needle, with the incorporation of a passive needle stick protection feature. The protection feature is made of a base and arm with a hinge on one end. As the needle is removed from a portal the hinge allows the needle to be pulled back and locked into the capture well in the base. There is an audible "click" when the needle is captured. The device is designed to help protect against exposure to bloodborne pathogens caused by accidental needlestick injuries. It does not protect against other routes of bloodborne pathogen transmission. The needle tip is fully protected after deployment of the needle stick protection feature and will not disengage with normal handling such that the needle tip becomes exposed.

The needle will be offered in various needle gauges and lengths, and with or without a slit-septum injection site cap.

The provided document describes the GRIPPER PLUS™ Needle, a medical device designed to protect against needlestick injuries. Below is an analysis of its acceptance criteria and the study used to demonstrate compliance.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for the GRIPPER PLUS™ Needle are primarily functional, focusing on the successful activation of the needle-stick protection feature and the biocompatibility of its components.

2. Sample Size and Data Provenance for Test Set

• Sample Size: The document does not explicitly state a numerical sample size for the "simulated use test." Instead, it mentions that the device was "evaluated by health care professionals during a simulated use test."
• Data Provenance: The testing was "in-vitro testing and simulated use testing." The country of origin for the data is not explicitly stated, but the submission is to the U.S. FDA, suggesting the testing was conducted to U.S. standards. The study was retrospective in the sense that the testing was performed to support the 510(k) submission after the device's design.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

• Number of Experts: The document states the device was "evaluated by health care professionals." It does not specify the exact number of professionals involved.
• Qualifications of Experts: The qualifications are broadly stated as "health care professionals." Specific expertise (e.g., years of experience, specific medical field) is not detailed.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (like 2+1 or 3+1 consensus). The assessment appears to be based on the direct observation and feedback from the "health care professionals" during the simulated use test, where the protection feature "successfully activated during all attempts."

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No multi-reader multi-case (MRMC) comparative effectiveness study was performed or cited. The document does not mention any comparison of human readers' performance with and without AI assistance, as AI is not a component of this device.

6. Standalone Performance Study

Yes, a standalone study was performed. The "functional testing" and "simulated use testing" evaluated the device's inherent performance. The key finding from this standalone testing was that the device's protection feature "successfully activated during all attempts."

7. Type of Ground Truth Used

The ground truth used for the acceptance criteria was primarily based on:

• Functional performance standards: The device's ability to activate its safety mechanism and protect the needle tip.
• Expert evaluation/observation: Healthcare professionals' assessment of the device's acceptability in simulated use.
• Biocompatibility testing: Laboratory results confirming material safety.

8. Sample Size for the Training Set

This device is a mechanical medical device, not an AI/machine learning algorithm. Therefore, there is no "training set" in the context of data used for algorithm development.

9. How Ground Truth for Training Set Was Established

Not applicable, as this is not an AI/machine learning device.

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Amber DU provides the Radiologist the ability to acquire chest images on film, to digitize these images real time for analysis on the Operator Workstation and retain the ability to analyze the films on a conventional lightbox, and to export the digital images Dicom 3 conformant to the hospital's network server for storage and eventual retrieval if desired.

The Oldelft Amber DU is comprised of the currently marketed Oldelft Amber AU, the Lumisys Lumiscan 75 LASER Film Digitizer which is commercially available, and a digital workstation based on a commercially available workstation from Rogan Imaging Corporation using software written by Rogan to Oldelft's specifications. The workstation displays screens to the operator for data input and for data and image display. The Oldelft Amber DU display is configured as a shell about the Rogan software which operates in the background to perform essential functions and to provide miniPACS features to Amber DU and allow the importation of patient demographic information and to allow the exportation of digital image data into which patient demographics have been incorporated.

The provided document is a 510(k) submission for the Oldelft Amber DU, primarily focusing on its regulatory approval as a Class II medical device. While it describes the device's components and intended use, it does not contain detailed information about acceptance criteria and a study proving the device meets those criteria, as typically found in clinical performance studies.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can and cannot be extracted based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table of acceptance criteria or reported device performance metrics (e.g., sensitivity, specificity, accuracy, F1 score). Its focus is on demonstrating substantial equivalence to a predicate device and adherence to general controls and performance standards for stationary X-ray systems (21 CFR 1020.30 and 21 CFR 1020.31). The "Indications for Use" section describes the functionality of the device rather than quantitative performance.

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

Not provided in the document.

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

Not provided in the document. The document mentions "Radiologist" in the indications for use, implying the device is for use by such professionals, but it doesn't describe expert involvement in a test set ground truth establishment.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

Not provided in the document.

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 provided in the document. The Amber DU combines existing technologies like an X-ray system, a film digitizer, and a workstation with software. The submission does not describe it as an "AI" device as we understand it today, nor does it detail MRMC studies or human reader improvement data.

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

Not provided in the document. The device is described as an "Operator Workstation" that displays screens for "data input and for data and image display," implying a human-in-the-loop operation, not a standalone algorithmic performance.

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

Not provided in the document.

8. The sample size for the training set:

Not applicable or not provided. Given the device's description as a combination of existing hardware and software for image acquisition, digitization, and display, it's unlikely to have a "training set" in the context of a machine learning algorithm.

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

Not applicable or not provided.

In summary: The provided document is a regulatory submission for premarket notification (510(k)) that focuses on demonstrating "substantial equivalence" of the Amber DU to a legally marketed predicate device. It addresses regulatory classification, standards, and intended use, but it does not include the detailed performance study results, acceptance criteria, or ground truth establishment methodologies that would be expected for a device making claims about performance benchmarks or AI capabilities. This type of information is typically found in clinical study reports, which are not part of this 510(k) summary.

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