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The Rumble Tuff Electric Breast Pump is an electrically powered single-user device used to express and collect milk from the breasts of lactating women. The device is not intended for hospital use.

The Rumble Tuff Electric Breast Pump is a powered Breast Pump. With different models, the Pumping can be performed on one breast or on both breasts at the same time; the Rumble Tuff Electric Breast Pump can be powered by 4 AA batteries, one 7.4V Li batteries or an AC Adapter provided with the pump. The pumping system consists of a diaphragm-type vacuum pump which is driven by a microcontroller controlled DC electric motor. Rumble Tuff Electric Breast Pump has five models; PA200S, PA201S, PA201D, PA203S, and PA203D. The suffix "S" stands for the model with single collection kit, and "D" stands for the model with dual collection kits. The user interface of PA200S uses 4 LEDs for indicating pumping cycle and uses a roller type adjuster for pumping level adjustment and indicating pumping level. Models PA201S, PA201D, PA203S, and PA203D all use the same user interface, a LCD screen for showing the pumping information; it also uses a roller type adjuster for pumping level adjustment. For all models, the user is able to control the cycle speed and vacuum level.

Given the provided document, the "acceptance criteria" and "study proving the device meets the acceptance criteria" pertain to the performance specifications of the medical device (Rumble Tuff Electric Breast Pump) and the testing conducted to demonstrate its performance and safety, primarily for substantial equivalence to predicate devices under a 510(k) submission.

Here's an analysis based on the provided text, formatted to address your specific points:

Since this document is a 510(k) summary for a breast pump, the "acceptance criteria" are the performance specifications and safety standards it must meet to be considered substantially equivalent to a legally marketed predicate device. The "study that proves the device meets the acceptance criteria" refers to the testing performed and summarized in the application to demonstrate these aspects.

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

The document provides the performance specifications for the Rumble Tuff Electric Breast Pump models. These are the reported device performance and implicitly serve as the acceptance criteria because the device is being presented as meeting these specifications for substantial equivalence.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states, "Additionally, the tests of vacuum levels and cycle speeds conducted by Acute Ideas are provided to demonstrate the performance of Rumble Tuff Electric Breast Pump."

• Sample Size: The specific sample size for performance testing (e.g., number of units tested for vacuum levels and cycle speeds) is not specified in this document.
• Data Provenance: The tests were "conducted by Acute Ideas," which is based in Taipei, Taiwan. The document does not explicitly state whether the testing was prospective or retrospective, but typically, performance verification testing for a new device submission would be 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)

This document describes a medical device's performance validation (a breast pump), not an AI algorithm requiring expert ground truth for interpretation (like medical images). Therefore, this question is not applicable in the context of this device and submission. Performance is measured against physical specifications and international safety standards, not expert consensus on qualitative data.

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

Not applicable for device performance testing, which involves objective measurements against specifications rather than subjective interpretation requiring adjudication.

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

Not applicable. This is a breast pump, not an AI-assisted diagnostic device where MRMC studies are relevant.

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

Not applicable. This is a physical medical device, not an algorithm. Performance is inherent to the device's mechanical and electrical functions.

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

The "ground truth" for this device primarily relies on objective physical measurements (e.g., vacuum pressure, cycle speed) against established engineering and medical device standards (e.g., IEC 60601-1, ISO 10993) and comparison to the performance of predicate devices. There is no qualitative "ground truth" in the diagnostic sense.

8. The sample size for the training set

Not applicable. This is not a machine learning or AI device that requires a training set.

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

Not applicable as there is no training set for this type of device.

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The Device measures the human body temperature from oral, axillary, and rectal. -The device is reusable and intended for clinical or home use on people of all ages.

The Digital Thermometer is an electronic clinical thermometer intended for the determination of oral, axillary, and rectal body temperature determination in humans. Classic type and Flexible type have the same indication for use; however, Flexible type has a flexible tip.

Here's an analysis of the provided 510(k) summary regarding the acceptance criteria and the study conducted for the Digital Thermometer:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The provided document does not contain information about a test set sample size or data provenance for human clinical studies. It explicitly states: "Controlled human clinical studies ware not conducted using the Digital Thermometer Classic type and Flexible type and predicate devices."

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

Since no human clinical studies were conducted, there were no experts used to establish ground truth for a test set. The substantial equivalence was based on conformance to recognized standards.

4. Adjudication Method for the Test Set

As no human clinical studies were conducted, no adjudication method was used for a test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

This device is a digital thermometer, not an AI-powered diagnostic tool. Therefore, no MRMC comparative effectiveness study was done regarding human readers and AI assistance, and there is no effect size to report.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

This device is a digital thermometer, not an algorithm. Therefore, no standalone algorithm performance testing was done. The performance was assessed against recognized physical and operating standards.

7. The Type of Ground Truth Used

The "ground truth" for the device's performance was established by conformance to internationally recognized standards for electronic thermometers (ASTM E1112, IEC 60601-1, IEC 60601-1-2). These standards define acceptable levels of accuracy, safety, and electromagnetic compatibility.

8. The Sample Size for the Training Set

This device is a digital thermometer, not a machine learning model. Therefore, there is no concept of a "training set" or "sample size for a training set" in this context.

9. How the Ground Truth for the Training Set Was Established

As explained above, there is no training set for this device. The "ground truth" for the device's design and manufacturing was established by the requirements and specifications outlined in the referenced international standards. Developers ensure their device can meet these established, industry-accepted criteria through design, testing, and manufacturing processes, rather than through a machine learning training paradigm.

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