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The Entuit™ Thrive Balloon Retention Gastrostomy Feeding Tube is intended to provide gastric access for enteral feeding, medication and decompression through an established gastrointestinal stoma tract.

The Entuit™ Thrive Balloon Retention Gastrostomy Feeding Tube is indicated for use in percutaneous placement of an enteral feeding tube in adult and pediatric patients that require enteral feeding, medication administration or decompression through an established gastrointestinal stoma tract.

The Entuit The Thrive Balloon Retention Gastrostony Feeding Tube is constructed primarily of silicone. The device consists of a tri-port funnel which is overmolded onto a previously extruded bi-lumen shaft. A retention bolster is molded separately and placed over the shall. At the proximal end, a silicone balloon is bonded to the shaft using silicone RTV. The tip of the shaft is filled with radiopaque barium RTV. In use, the balloon end of the catheter is inserted through a gastrointestinal stoma tract and into the stomach lumen. The balloon is then inflated to the rated volume using sterile or distilled water and the bolster adjusted down to the skin to secure the device in place.

The acceptance criteria and study proving the device meets them are described below:

1. Acceptance Criteria and Reported Device Performance

• Balloon Burst Volume
• Balloon Volume Maintenance
• Balloon Concentricity
• Balloon Over Shaft Size
• Balloon Integrity
• Balloon Integrity in simulated gastric fluid | All tests were conducted in accordance with ASTM F2528-06 (Procedures A through G). Where specific acceptance criteria were not defined in the standard, the candidate device's performance was required to be equivalent to or better than the predicate device. | The candidate device met all performance requirements. Where acceptance criteria were not defined in the standard, the candidate device performed equivalent to or better than the predicate device. |

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). The testing appears to be primarily bench testing of the physical device.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not applicable as the study involved bench testing of a medical device against a technical standard (ASTM F2528-06), not a diagnostic study requiring human expert interpretation for ground truth.

4. Adjudication Method for the Test Set

This information is not applicable as the study involved bench testing against a technical standard, not a diagnostic study requiring human expert adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

No MRMC comparative effectiveness study was mentioned. The study described is bench testing comparing the performance of the submitted device to a predicate device and a technical standard.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

This question is not applicable as the device is a physical medical device (gastrostomy feeding tube), not an AI algorithm. The performance evaluation was standalone in the sense that the device's technical specifications were tested in a controlled environment.

7. The Type of Ground Truth Used

The ground truth for the device's performance was established by adherence to a recognized international standard, ASTM F2528-06 (Procedures A through G), for medical gastrostomy tubes. For aspects where the standard did not define specific criteria, the predicate device's performance served as the benchmark for equivalence.

8. The Sample Size for the Training Set

This information is not applicable as the device is a physical medical device, not an AI model that requires a training set.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable as the device is a physical medical device, not an AI model that requires a training set with established ground truth.

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