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510(k) Data Aggregation
(138 days)
JuellCure Hard is intended for use as a permanent hard relining for total and partial dentures. Hard permanent total or partial relining for restoring partial and complete dentures. Lengthening denture margins.
JuellCure hard is a cold-curing, hard relining material for permanently relining dentures.
The provided text describes the 510(k) summary for the JuellCure Hard, a hard impression material for relining dentures. The submission aims to demonstrate substantial equivalence to a predicate device, Ufi Gel Hard C (K030916).
Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Characteristic | Acceptance Criteria (Predicate Device K030916) | Reported Device Performance (JuellCure Hard) |
|---|---|---|
| Setting Time | 135-180 seconds | 135-180 seconds |
| Appearance | Paste is a colorless, homogenous liquid and the catalyst is a white homogenous paste. | Paste is a colorless, homogenous liquid and the catalyst is a white homogenous paste. |
| Composition of Catalyst | Matrix 40-60%, Filler 30-40%, Catalyst 2-4%, Modifier 6-7%, Stabilizer <0.1% | Matrix 40-60%, Filler 30-40%, Catalyst 2-4%, Modifier 6-7%, Stabilizer <0.1% |
| Composition of Paste | Monomer: HEDMA, UDMA, Bis-GMA 30-50%, Filler 40-60%, Colorant <0.1%, Modifier 5-6%, Stabilizer <0.1% | Monomer: HEDMA, UDMA, Bis-GMA 30-50%, Filler 40-60%, Colorant <0.1%, Modifier 5-6%, Stabilizer <0.1% |
| Flexural Strength | 78 Mpa | 78 Mpa |
| Translucency of 2mm specimens | 44% | 44% |
| Adhesion to denture material | Implied to be similar to predicate | Implied to be similar to predicate |
| Thermocycling | Implied to be similar to predicate | Implied to be similar to predicate |
| Color stability | Implied to be similar to predicate | Implied to be similar to predicate |
| Heat of polymerization | Implied to be similar to predicate | Implied to be similar to predicate |
Note: For "Adhesion to denture material," "Thermocycling," "Color stability," and "Heat of polymerization," the document states: "The values for these performance characteristics was found to be very similar to the predicate device." While specific numerical acceptance criteria are not explicitly given for these, the "very similar" statement implies the JuellCure Hard met an unstated acceptance threshold based on predicate performance.
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify the sample size used for performance testing (e.g., for flexural strength, translucency, etc.) nor does it explicitly mention data provenance (e.g., country of origin, retrospective or prospective). The testing appears to be laboratory-based evaluation of material properties, not clinical data from patients.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the document. The performance testing described relates to the physical and chemical properties of the material, which would be measured in a lab setting, not typically evaluated by clinical experts in the same way, for example, diagnostic imaging studies would be.
4. Adjudication Method (e.g. 2+1, 3+1, none) for the Test Set
The concept of an "adjudication method" (like 2+1 or 3+1 consensus) is primarily relevant for studies involving subjective human interpretation, such as medical image reading. For laboratory-based performance testing of a material, this method is not applicable and therefore not mentioned in the submission.
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
An MRMC study is relevant for evaluating the impact of AI on human reader performance in tasks like diagnostic interpretation. This submission is for a dental material (hard impression material), not an AI-powered diagnostic device. Therefore, an MRMC study was not conducted and is not applicable.
6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done
This is not an AI-powered device. Therefore, a standalone (algorithm-only) performance evaluation was not done and is not applicable.
7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)
The "ground truth" for the performance testing mentioned (e.g., flexural strength, setting time, translucency) would be established through standardized material testing methods and measurements. For instance, flexural strength would be measured using an appropriate mechanical testing machine, and setting time would be determined according to an established dental materials standard. The comparison is made against the reported performance of the predicate device.
8. The Sample Size for the Training Set
This question is relevant for machine learning or AI-based devices. The JuellCure Hard is a material, not an AI device, so there is no training set in this context.
9. How the Ground Truth for the Training Set Was Established
As there is no training set, this question is not applicable.
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