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510(k) Data Aggregation
(659 days)
The reusable Maxima Surgical Gown is intended to be used by operating room personnel during surgical procedures to help protect both the patient and the health care worker from transfer of micro-organisms, body fluids, and particulate material.
The reusable Maxima Surgical Gown is provided non-sterile. Sterilization parameters are as follows: * Prevac Steam Sterilization - 135°C/274°F (temperature) / 3 minutes (exposure time) / 20 minutes (dry time)
The Maxima® Surgical Gown is a reusable woven surgical gown for use in sterile conditions. It is made of 100% filament polyester that provides AAM PB70:2012 Level 3 Liquid Barrier Performance Barrier in the critical zone up to 75 reprocessing cycles. The back of the gown provides AAMI Level 3 Liquid Barrier Performance Barrier up to 75 reprocessing cycles. The seam in the sleeves are sealed with a permanent seam tape which seals the stitch area enabling it to perform as AAMI Level 3 up to 75 reprocessing cycles.
Maxima® Surgical Gown will be manufactured in small, medium, and larges sizes. Maxima® Surgical Gowns will be sold as non-sterile reusable surgical gowns that are to be laundered, sterilized and processed by health care facilities and or contract laundry and sterliization companies before first use and after each subsequent use.
This document is a 510(k) Premarket Notification for a surgical gown, not an AI medical device. Therefore, the questions related to AI device performance, training sets, ground truth establishment, expert consensus, and MRMC studies are not applicable.
The document focuses on demonstrating that the Maxima Surgical Gown is substantially equivalent to a predicate device (Blockade Surgical Gown) by meeting relevant performance and safety standards.
Here's an analysis of the acceptance criteria and performance study as presented for this surgical gown:
1. A table of acceptance criteria and the reported device performance:
| Test Conducted | Test Standard | Acceptance Criteria | Reported Device Performance |
|---|---|---|---|
| Cytotoxicity ISO MEM Elution Using L-929 Mouse Fibroblast Cells | ISO 10993-5 Cytotoxicity: Biological Evaluation Of Medical Devices - Part 5: Tests For In Vitro Cytotoxicity | non-cytotoxic | non-cytotoxic |
| Skin Irritation ISO Intracutaneous Irritation Test | ISO 10993-10 Irritation: Biological Evaluation of Medical Devices - Part 10: Tests For Skin Sensitization | non-irritating | non-irritating |
| Skin Sensitization ISO Guinea Pig Buehler Sensitization Test | ISO 10993-10 Sensitization: Biological Evaluation of Medical Devices - Part 10: Tests For Skin Sensitization | non-sensitizing | non-sensitizing |
| Water Resistance (Hydrostatic Pressure) | AATCC 127: Test Method for Water Resistance: Hydrostatic Pressure | >50 cm AQL 4.0 | Avg. 70.3 cm |
| Water Resistance (Impact Penetration) | AATCC 42: Test Method for Water Resistance: Impact Penetration | 30 lbf | Warp: 138.9 lbf, Fill: 82.01 lbf |
| Tear Strength (Trapezoid Procedure) | ASTM D 5587: Standard Test Method for Tearing Strength of Fabrics by Trapezoid Procedure | >1.5 lbf | Warp: 25.3 lbf, Fill: 7.4 lbf |
| Seam Strength (Failure In Sewn Seams) | ASTM D1683: Standard Test Method For Failure In Sewn Seams Of Woven Fabrics | >30 lbf | Warp: 78 lbf, Fill: 65.4 lbf |
| Weight | ASTM D3776/D3776M-09a: Standard Test Methods For Mass Per Unit Area (Weight) Of Fabric | Documentation Only | 88 gsm |
| Flammability | 16 CFR 1610: Standard for the Flammability of Clothing Textiles | Class 1 | Class 1 |
| Lint Generation | EN ISO 9073-10: Textiles - Test methods for nonwovens - Part 10: Lint and other particles generation in the dry state | Documentation Only | log10 (lint count) 1.89 |
| Evaporative Resistance | ASTM F1868: Standard Test Method for Thermal and Evaporative Resistance of Clothing Materials Using a Sweating Hot Plate | Documentation Only | Ret Critical Zone 0.01193, Ret Noncritical Zone 0.01157 |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
The document does not explicitly state the sample sizes (e.g., number of gowns or fabric samples) used for each specific test in the "Summary of Non-Clinical Testing" section. It refers to general "performance testing." The data provenance (country of origin, retrospective/prospective) is also not specified, as this is a laboratory performance testing rather than a clinical study with patient data.
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):
Not applicable. This is a physical product performance validation based on defined test standards, not an AI or diagnostic device requiring expert interpretation for ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. This is not a study requiring adjudication of expert opinions.
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 a surgical gown, not an AI-assisted diagnostic device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. This is a physical product.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc):
The "ground truth" for this device is the adherence to established industry-wide performance standards and specifications for surgical gowns, such as those related to barrier protection (AAMI Level 3), biocompatibility (ISO 10993 series), and physical properties (ASTM standards). The tests themselves provide objective measurements against these predefined criteria.
8. The sample size for the training set:
Not applicable. This is not a machine learning model.
9. How the ground truth for the training set was established:
Not applicable. This is not a machine learning model.
In summary: The provided document is a 510(k) summary for a surgical gown, demonstrating substantial equivalence to a predicate device through detailed non-clinical performance testing against established industry standards. It does not involve AI, clinical data, expert consensus, or typical AI/software device evaluation metrics.
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