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510(k) Data Aggregation
(11 days)
Intended for use as a varnish on sensitive teeth over exposed dentin under temporary restoratives and cements where post-operative sensitivity is a concern and to improve quality and functionality of restorations when used in conjunction with dental restoratives and cements. To seal dentinal tubules in cavity preparations or on sensitive root surfaces.
The modified varnish has the following similarities to that which previously received 510(k) concurrence:
- Has the same active ingredient at the same concentration; .
- has the same indications for use; .
- incorporate the same or similar materials; .
- has the same shelf life, and; ●
- is packaged using the same materials and processes.
I am sorry, but the provided text does not contain information regarding acceptance criteria, device performance, study details, or ground truth establishment. The document is primarily a 510(k) summary and an FDA clearance letter for a dental varnish, outlining its substantial equivalence to a predicate device and its indications for use. It does not include the results of a clinical study or performance testing.
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(77 days)
Int inded for use as a varnish on sensitive teeth over exposed dentin under temporary restoratives and cements where post-operative sensitivity is a concern and to improve quality and functionality of restorations when used in conjunction with dental restoratives and cements. To seal dentinal tubules in cavity preparations or on sensitive roc t surfaces.
Not Found
This 510(k) summary for K030488, "FLUORILAQ Fluoride Varnish," does not contain the detailed information required to describe acceptance criteria or a study proving the device meets those criteria in the way a contemporary medical device submission for an AI/ML product would.
The document is for a dental varnish, which is a Class II medical device, and its approval is based on substantial equivalence to existing predicate devices, not on performance metrics derived from clinical studies with ground truth establishment in the context of AI.
However, I can extract information related to how substantial equivalence was determined, which serves a similar purpose to demonstrating "meeting acceptance criteria" in this context.
Here's an attempt to answer your questions based on the provided text, while acknowledging its limitations for an AI/ML context:
1. A table of acceptance criteria and the reported device performance
For this type of device (dental varnish), "acceptance criteria" are generally related to demonstrating similar intended use and technological characteristics to legally marketed predicate devices, rather than quantitative performance metrics like sensitivity, specificity, or accuracy.
| Acceptance Criteria (for Substantial Equivalence) | Reported Device Performance (as stated in 510(k) Summary) |
|---|---|
| Intended Use: Device has the same intended use as predicate devices. | Fluorilaq has the same intended use: as a varnish on sensitive teeth over exposed dentin under temporary restoratives and cements and exposed dentin on roots. |
| Technological Characteristics: Device has the same or similar technological characteristics as predicate devices. | The technological characteristics for Fluorilaq are the same as those for the predicate devices and other resinous products currently on the market, except for minor variations in the same or similar components. |
| Materials: Device is made from substantially equivalent materials as predicate devices. | Descriptive information provided shows that the materials from which Pascal Co., Inc.'s Fluorilaq is made are substantially equivalent to (nearly identical with some) those of similar products, used for identical purposes, currently on the market. |
| Safety and Effectiveness: No new questions of safety and effectiveness are raised by the differences between the device and the predicate. | Not explicitly stated as a "performance" metric, but the FDA's clearance implies this criterion was met as it found the device substantially equivalent. |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
This information is not provided in the document. For a dental varnish, substantial equivalence is typically established through a comparison of product specifications, materials, and intended use, often without a formal "test set" in the sense of a clinical or imaging study.
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 information is not provided and is not applicable to a 510(k) submission for a dental varnish. No "ground truth" in the context of expert consensus was established for this device's approval process.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
This information is not provided and is not applicable.
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 information is not provided and is not applicable. This is not an AI/ML driven device.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
This information is not provided and is not applicable. This is not an AI/ML driven device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
There was no formal "ground truth" used in the context of a clinical study for this device's 510(k) clearance. Substantial equivalence was based on comparison to legally marketed predicate devices by evaluating intended use, technological characteristics, and materials.
8. The sample size for the training set
This information is not provided and is not applicable. There was no "training set" in the context of an AI/ML algorithm.
9. How the ground truth for the training set was established
This information is not provided and is not applicable. There was no "training set" or "ground truth" for it in the context of an AI/ML algorithm.
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