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510(k) Data Aggregation
(10 days)
BLUE ORTHOCORD SUTURE
Blue ORTHOCORD suture is indicated for use in general soft tissue approximation and/or ligation, including use in orthopedic surgeries.
Blue ORTHOCORD suture is a synthetic, sterile, braided composite suture composed of dyed (D&C Blue #6) absorbable polydioxanone (PDS) and un-dyed non-absorbable polyethylene. The partially absorbable suture is coated with a copolymer of 90% caprolactone and 10% glycolide.
1. Acceptance Criteria and Reported Device Performance
The provided document is a 510(k) summary for a medical device (Blue ORTHOCORD suture) and focuses on demonstrating substantial equivalence to predicate devices, rather than establishing specific quantitative acceptance criteria or reporting detailed performance metrics against those criteria in the format typically found for diagnostic or interventional devices with quantifiable outcomes like accuracy, sensitivity, or specificity.
For medical sutures, "acceptance criteria" and "device performance" are typically evaluated against established consensus standards and the USP monograph for absorbable sutures. The document states:
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Conformance to consensus and voluntary standards. | Non-clinical laboratory testing was performed demonstrating that the device conformed to the USP monograph for absorbable sutures. |
| Substantial equivalence to predicate devices (Violet ORTHOCORD suture and PDS II suture) based on technological characteristics and indications for use. | Based on the indications for use, technological characteristics, and comparison to predicate devices, the Blue ORTHOCORD suture has been shown to be substantially equivalent to predicate devices under the Federal Food, Drug and Cosmetic Act. |
2. Sample Size and Data Provenance
The document describes non-clinical laboratory testing. For such testing, the concept of "test set" in the context of diagnostic AI models is not directly applicable. The "sample size" would refer to the number of sutures tested for various properties (e.g., tensile strength, knot security, degradation profile). However, the specific sample sizes used for the non-clinical laboratory testing are not provided in this 510(k) summary.
The data provenance is also not explicitly stated in terms of country of origin or whether it was retrospective or prospective, as these distinctions are more relevant for clinical studies with human subjects or real-world data collection. The testing was "non-clinical laboratory testing."
3. Number of Experts and Qualifications for Ground Truth
This document does not involve the establishment of "ground truth" by human experts in the way an AI diagnostic study would. The performance of a suture is determined through objective, standardized laboratory tests (e.g., against USP monographs). Therefore, there were no experts used to establish ground truth in this context. The "ground truth" is defined by the technical specifications of the materials and the performance parameters set by regulatory and industry standards.
4. Adjudication Method
Since no human experts were involved in establishing ground truth, there was no adjudication method used.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study was done. This type of study is typically conducted for diagnostic devices where human readers interpret medical images or data, and their performance with and without AI assistance is compared. A suture is a physical medical device, not an interpretive one.
6. Standalone (Algorithm Only) Performance Study
No standalone performance study (algorithm only) was done. The device is a physical suture, not an algorithm.
7. Type of Ground Truth Used
The "ground truth" in this context is based on objective measurements derived from non-clinical laboratory testing against established specifications and standards, specifically the USP monograph for absorbable sutures. It's not based on expert consensus, pathology, or outcomes data in the way a diagnostic AI product would be.
8. Sample Size for Training Set
There is no concept of a "training set" for this device. A training set is used to train machine learning models. This is a physical medical device, not an AI or software algorithm.
9. How Ground Truth for Training Set Was Established
As there is no training set, this question is not applicable.
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