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510(k) Data Aggregation
(124 days)
The Standard Clamp is indicated for use in laparoscopic procedures to grasp, clamp, and manipulate soft tissues.
The Standard Clamp is a reusable, non-energized, stainless steel surgical instrument designed for use in laparoscopic procedures to clamp long planes of soft tissue such as the stomach. The instrument is comprised of three main sections: handle, shaft, and end effector. The handle is an inline grip with two knobs which are manually rotated in order to manipulate the end effector. The shaft is sized to fit a standard 12 mm trocar. The end effector of the clamp is comprised of an upper and lower jaw that close parallel in order to grasp tissue.
The provided text describes a 510(k) premarket notification for a medical device called the "Standard Clamp." This document focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a study proving device performance against those criteria in the context of an AI/ML algorithm.
Therefore, many of the requested details about acceptance criteria, study design for AI/ML performance, sample sizes for training/test sets, expert adjudication, MRMC studies, standalone performance, and ground truth establishment for AI/ML are not available in this document.
However, I can extract the information related to the device's pre-clinical evaluation which serves as a form of performance data for this physical medical device.
1. A table of acceptance criteria and the reported device performance
The document does not provide a formal table of quantifiable acceptance criteria with corresponding performance metrics for the Standard Clamp. Instead, it describes a pre-clinical evaluation and its outcome.
| Acceptance Criteria (Implied) | Reported Device Performance (Summary) |
|---|---|
| Ability to clamp long planes of soft tissue without damage | "Testing demonstrated acceptable performance of the Standard Clamp." "No damage to surrounding tissues were caused by any of the clamps." |
| Histological tissue effects comparable to control/predicate devices | "The histology demonstrated similar types of tissue effects between the three clamps." |
| Conformance to device specifications | "The data demonstrate conformance to the device specifications." |
2. Sample sized used for the test set and the data provenance
- Sample Size: Not explicitly stated as a number of animals or trials. The text mentions "a live anesthetized pig" (singular) and "the porcine stomach laparoscopically." It then refers to "histology samples... for each clamp," implying multiple clamping actions or multiple clamps tested on the same animal. The exact number of clamping instances or samples is not specified.
- Data Provenance: Prospective, in-vivo animal study. The location (country of origin) is not specified, but the submission is to the U.S. FDA.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This information is not provided. The histological analysis would typically be performed by a veterinary pathologist, but their number and qualifications are not mentioned.
4. Adjudication method for the test set
Not applicable/not described. This was a direct observation and histological analysis, not a human reader study requiring adjudication.
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
No, an MRMC study was not done. This device is a surgical instrument, not an AI/ML diagnostic tool, so this type of study is not relevant.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is a physical surgical instrument.
7. The type of ground truth used
The ground truth was established through:
- In-vivo observation of tissue effects during and after clamping.
- Histological analysis of resected stomach tissue after clamping.
8. The sample size for the training set
Not applicable. This is a physical device, not an AI/ML model that requires a training set.
9. How the ground truth for the training set was established
Not applicable.
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