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510(k) Data Aggregation
(49 days)
Twinky Star NF is intended for fillings of deciduous teeth.
Twinky Star NF is a light-curing, colored, radiopaque and fluoride containing compomer filling system for cavities of deciduous teeth.
The provided document is a 510(k) summary for a dental filling material called "Twinky Star NF." It declares substantial equivalence to a predicate device and states its intended use. However, it does not contain any information about acceptance criteria, a specific study proving the device meets those criteria, or any performance metrics, sample sizes, or ground truth establishment relevant to the questions asked.
The document primarily focuses on establishing substantial equivalence based on the technological characteristics and prior use of components in legally marketed devices, rather than presenting a performance study with acceptance criteria.
Therefore, I cannot provide a detailed answer to your request based on the given input, as the information is not present in the document.
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(48 days)
Twinky Star is a compomer filling material for deciduous teeth.
Not Found
The provided text is related to an FDA 510(k) clearance for a dental filling material called "Twinky Star." This document is a regulatory approval letter and does not contain information about acceptance criteria, device performance studies, or details relevant to artificial intelligence (AI) or machine learning (ML) models. It discusses the device's classification, applicable regulations, and approval for marketing based on substantial equivalence to a predicate device.
Therefore, I cannot provide the requested information. The document does not describe:
- A table of acceptance criteria and reported device performance.
- Sample sizes, data provenance, or details of a test set.
- The number or qualifications of experts used for ground truth.
- Adjudication methods.
- MRMC comparative effectiveness studies or effect sizes of AI assistance.
- Standalone algorithm performance.
- The type of ground truth used (expert consensus, pathology, outcomes data, etc.).
- Sample size for the training set.
- How ground truth for the training set was established.
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