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Straumann® Screw Retained Abutments are indicated to be placed into the implants of the Straumann® Dental Implant System to provide support for prosthetic reconstructions such as crowns, bridges and bars. The final processed devices have the purpose of restoring chewing function. Straumann® Screw Retained Abutments are indicated for screw-retained restorations.

Straumann® Screw Retained Abutments include one-piece straight and angled (17° and 30°) abutments, basal screws and abutment carrier pin. The subject device is identical to the primary predicate, K133421, and subsequent premarket notification submissions K141871, K150814 and K151247 with the exception that the subject device will be delivered to the user sterile as opposed to non-sterile and a change in packaging to ensure sterility of the devices.

This document is a 510(k) premarket notification for the Straumann® Screw Retained Abutments. It aims to demonstrate substantial equivalence to previously cleared predicate devices. Since this is not a diagnostic device and doesn't involve an AI algorithm, the information requested in your prompt (acceptance criteria, study details, expert input, MRMC study, standalone performance, training set) typically applies to AI/ML-based medical devices or diagnostic tools.

This submission focuses on engineering, material, and sterilization equivalence for a dental implant component. Therefore, the detailed AI/ML-specific questions in your prompt are not applicable to this document.

However, I can extract information related to the device's technical characteristics and the claim of substantial equivalence.

Here's a breakdown of what is applicable from your prompt, based on the provided text:

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

The document doesn't explicitly state "acceptance criteria" in the format typically used for performance metrics of diagnostic algorithms. Instead, it relies on demonstrating substantial equivalence to predicate devices. This means that the device's characteristics must be similar enough to the predicate to ensure equivalent safety and effectiveness.

The table provided (pages 5-6) serves as the primary evidence for this. It compares the Subject Device (Straumann® Screw Retained Abutments) to several Predicate Devices across various technical characteristics. The "performance" in this context is demonstrating that these characteristics are either identical or sufficiently similar to the legally marketed predicates.

The study proving the device meets its "acceptance criteria" (i.e., substantial equivalence) is the comparison to predicate devices and the verification/validation activities for the changes (primarily sterilization and packaging). The document explicitly states: "The subject device is identical to the primary predicate, K133421, and subsequent premarket notification submissions K141871, K150814 and K151247 with the exception that the subject device will be delivered to the user sterile as opposed to non-sterile and a change in packaging to ensure sterility of the devices." And "Packaging materials and configuration is equivalent to reference predicate device, Straumann® Healing Abutments, Healing Caps, Closure Screws, K130808."

The substantial equivalence is primarily based on:

• Identical Indications for Use.
• Identical fundamental operating principles.
• Identical materials (Ti-6Al-7Nb titanium alloy).
• Comparable design features (Implant-to-Abutment Connection, Interface Type, Platform Diameter(s), Abutment Angulation(s), Gingival Height(s), Orientation of Angulation to Engagement Features).
• Validation of the sterilization process according to recognized standards (ISO 11137-1 and ISO 11137-2) and equivalence of packaging to a previously cleared sterile device (K130808).

The following points are not applicable to this 510(k) submission for a non-AI/ML dental abutment:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or 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)
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
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
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
8. The sample size for the training set
9. How the ground truth for the training set was established

These points would be relevant for devices that involve algorithms, image interpretation, or diagnostics where a "ground truth" needs to be established and performance metrics (e.g., sensitivity, specificity, AUC) are calculated based on a test set. This submission is for a physical medical device (dental abutment) where the primary evaluation is based on engineering design, material properties, and manufacturing processes, demonstrated through comparison to legally marketed predicate devices and validation of changes like sterilization.

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Prosthetic components directly or indirectly connected to the endosseous dental implant are intended for use as an aid in prosthetic rehabilitations.

The subject devices represent a line extension of the Straumann Dental Implant System (SDIS). The subject devices are an assembly of a noble metal alloy (Ceramicor®) Abutment Base and a polymer (POM) Modeling Aid. The Modeling Aid is attached to the abutment base by means of a friction fit. The subject devices employ the same Modeling Aid (catalog no. 049.217) as the identified predicate devices. The subject devices use the same Basal Screw for fixing the finished restoration to the implant (catalog no. 049.128) as the identified predicate devices. The subject devices interface with Straumann Tissue Level (TL) implants having the Regular Neck (RN) or Wide Neck (WN) implant-to-abutment interface. The subject devices do not engage the anti-rotation features within the TL implants. The non-engaging design makes these devices suitable for the fabrication of bar and bridge superstructures by the dental laboratory using either casting or soldering techniques.

This document is a 510(k) Premarket Notification for a dental abutment, specifically the "Straumann® synOcta Gold Abutments for Bridge." The purpose of this notification is to demonstrate that the new device is substantially equivalent to existing predicate devices already on the market, rather than proving a new medical claim or performance characteristic that would require extensive clinical trials. Therefore, the information provided focuses on the device's design, material, and mechanical performance compared to a predicate, not on complex acceptance criteria for a diagnostic algorithm or a clinical outcome.

Here's an analysis of the provided text in the context of the requested information, understanding that this is for a physical medical device (dental abutment) and not an AI/diagnostic tool:

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

The document does not present a formal table of "acceptance criteria" in the way one would for a diagnostic device (e.g., sensitivity, specificity, accuracy thresholds). Instead, it relies on demonstrating substantial equivalence to a predicate device through conformity to existing standards and showing comparable mechanical performance.

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 mentions "Dynamic fatigue test data" in support of the submission. However, it does not specify:

• The sample size for these tests.
• The specific data provenance (country of origin, retrospective or prospective).
• Details about a "test set" in the context of diagnostic performance. The testing here is mechanical (fatigue).

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 question is not applicable to this submission. The device is a physical dental abutment, and its "performance" is evaluated through mechanical bench testing and material characterization, not by expert interpretation or ground truth establishment in a clinical imaging or diagnostic context.

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

This question is not applicable. There is no "adjudication" in the sense of reconciling expert opinions for a diagnostic outcome. Mechanical tests are typically performed according to standardized protocols.

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

This question is not applicable. MRMC studies are used for diagnostic devices involving human readers and AI. This submission is for a physical dental abutment.

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

This question is not applicable. This is not an algorithm or AI device.

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

For this device, "ground truth" is established through:

• Mechanical standards: Adherence to international standards like ISO 14801 for dynamic fatigue testing.
• Material specifications: Conforming to material standards like ISO 22674 for metallic dental materials.
• Biocompatibility and Sterilization: Relying on the established safety of the materials and manufacturing methods used in the predicate devices.

8. The sample size for the training set

This question is not applicable. There is no "training set" as this is not an AI/machine learning device. The design, materials, and manufacturing processes are developed through engineering and materials science, not by training an algorithm on data.

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

This question is not applicable for the reasons stated above.

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