Search Results

The PARIEFIX Endoscopic Stapler is indicated for approximation of soft tissues and fixation of surgical mesh to tissues during laparoscopic surgical procedures such as hernia repair.

The PARIEFIX device is an Endoscopic Stapler composed of a disposable delivery instrument and resorbable fixation devices. The PARIEFIX Delivery Instrument consists of an ergonomic handle, trigger, locking/unlocking mechanism, rotation knob, shaft containing ten fixation devices, and retractable hollow needle. The trigger, locking/unlocking mechanism, rotation knob, and hollow needle all function in the delivery of the fixation device to the tissue. A visual marker at the distal tip of the shaft indicates the position of the retractable needle to aid in placement of the fixation device. The resorbable fixation device consists of a connection pin, which connects the distal anchoring tip to the proximal tip. The distal anchoring tip penetrates the biological tissues and, after back and forth motion, is anchored into the tissues. The proximal tip anchors the mesh to the biological tissues.

The provided text describes the Sofradim Production PARIEFIX™ Endoscopic Stapler. However, it does not contain detailed information about specific acceptance criteria, reported device performance metrics in a quantitative manner, sample sizes for test sets, data provenance, expert qualifications, adjudication methods, MRMC studies, standalone performance, or training set details.

The relevant section, "PERFORMANCE TESTING 7.", states:
"Testing was performed to determine the performance characteristics of the PARIEFIX Resorbable Fixation Devices in comparison with the predicate devices. The test results showed that the Sofradim and predicate devices were similar in performance characteristics."

This statement confirms that performance testing was done and that the device was found to be "similar" to predicate devices. However, it lacks the specific quantitative data needed to fill out the requested table and answer many of the subsequent questions.

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

Here's what can be extracted and what cannot:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size for test set: Not specified.
• Data provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• This information is not applicable as the performance testing described is for a physical medical device (endoscopic stapler) and not for an AI/diagnostic algorithm where expert-established ground truth would typically be required. The "ground truth" here would relate to mechanical performance, not diagnostic accuracy.

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

• Not applicable for the type of device and testing 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

• Not applicable; this is for a physical surgical device, not an AI diagnostic tool.

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

• Not applicable; this is for a physical surgical device, not an AI algorithm.

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

• The "ground truth" for this type of device would likely be based on engineering specifications, mechanical testing standards, and possibly in-vitro or ex-vivo performance metrics comparing staple strength, deployment reliability, material biocompatibility, and similar physical properties. The document states "Testing was performed to determine the performance characteristics," implying engineering and mechanical tests.

8. The sample size for the training set

• Not applicable for this type of device; this is not an AI/machine learning product.

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

• Not applicable for this type of device; there is no training set in the context of AI/machine learning.

Ask a specific question about this device