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Digital Thermometer is intended to measure the body temperature either oral or axillaries (under arms) and to be used by consumers in a household environment. The device is intended for repeatable use by one patient. It is intended for use on people of all ages.

The Digital Thermometer utilizes measured thermistor resistance and a microcontroller unit to determine and display human body temperature. Changes in the probe temperature due to oral or auxiliary contact correspond to changes in resistance. The probe Temperature values are digitally displayed on an LCD screen in 0.2 degrees Fahrenheit increments. The measurements do not account for a probe cover. The user contacting materials are Stainless steel and Acrylonitrile Butadiene Styrene (ABS).

The Digital Thermometer includes 3 models: DT001, DT007 and DT008. The differences of these models are in appearance and specifications. All of them have the same basic functions:

• Precision of 0.1℃ for measurements between 35℃ and 39℃ for measurement
• Data displayed in Celsius -
• Automatic stop (energy saver) -

The provided document is a 510(k) premarket notification for a Digital Thermometer. It describes the device, its intended use, and a comparison to a predicate device. It also mentions non-clinical tests performed to demonstrate substantial equivalence.

However, the document does not contain the specific acceptance criteria for performance metrics of the digital thermometer (e.g., specific accuracy limits for different temperature ranges as defined by the manufacturer for acceptance) and does not explicitly detail a study report showing the device meets predefined acceptance criteria for these specific metrics. It lists the regulated accuracy standard, but not the manufacturer's internal acceptance criteria.

Therefore, many of the requested items cannot be fully answered from this document. I will extract what is available and indicate when information is not present.

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

The document lists performance standards and typical accuracy ranges for clinical electronic thermometers. It does not provide a table of acceptance criteria set by the manufacturer for the specific device and then report the device performance against those criteria in a tabular format. Instead, it states that the device complies with the standards.

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. The document refers to "nonclinical testing performed in accordance with applicable standards and FDA guidance documents," but does not detail the specific sample size of devices tested or the data provenance for these tests.

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 is a physical medical device (digital thermometer) and the "ground truth" for its accuracy would typically be established by comparison to a traceable temperature standard or reference thermometer in laboratory conditions, not by expert interpretation. Therefore, this question is not applicable in the context of this device.

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

As per the answer to point 3, this is not applicable for a physical device like a digital thermometer. Adjudication methods are typically relevant for studies involving human interpretation (e.g., image analysis by radiologists).

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 is a physical medical device (digital thermometer) and its performance is evaluated in terms of accuracy and technical specifications, not human interpretive tasks. Therefore, an MRMC study is not applicable to this device.

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

The device is a digital thermometer. Its "standalone" performance refers to its ability to accurately measure temperature on its own. The document states "Performance tests protocols were performed in accordance with applicable standards... The results of the tests demonstrate the compliance to applicable thermometer standards." This implicitly indicates that the device's standalone performance was evaluated against these standards. The response time of 30s is also a standalone performance characteristic.

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

For a digital thermometer, the "ground truth" would be the actual temperature measured by a highly accurate, calibrated reference thermometer or established through a traceable temperature standard in a controlled laboratory environment. The document implies this type of ground truth was used by stating compliance with standards like ASTM E1112 and ISO 80601-2-56, which outline methods for calibrating and testing thermometer accuracy.

8. The sample size for the training set

This information is not provided in the document. Digital thermometers typically do not involve "training sets" in the sense of machine learning algorithms. The development involves engineering and calibration, not algorithm training on data.

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

As per the answer to point 8, the concept of a "training set" with ground truth as typically understood in AI/ML contexts is not applicable to a digital thermometer. Ground truth for calibration and testing would be established by reference to traceable temperature standards.

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The BR128 Breast Pump is an electrically powered suction device intended to express and collect milk from a lactating woman's breasts. This is for a single user.

The BR128 is an electrically powered breast pump that uses negative pressure to express milk from the breasts of lactating women. It adopts a Single-Chip Microcomputer to imitate a baby's suckling action and a reciprocating vacuum pump to generate the suction to extract the milk at vacuum levels between 0 and 170mmHg. It is intended for single pumping.

It is designed to mechanically interface with a mother's breast via a massage cushion and withdraw then collect the breast milk. The motor unit of the device is connected to the bottle via a silicone tube. Its suction strength is adjustable via the suction adjustment button on the device, but its cycles per minute are controlled by software and cannot be adjusted. The device can be powered by batteries or the provided mains adapter.

The provided text is a 510(k) Summary for the BR128 Breast Pump, which is an electrically powered suction device intended to express and collect milk from a lactating woman's breasts. This document is a premarket notification for a medical device submitted to the FDA, asserting substantial equivalence to a legally marketed predicate device.

Based on the provided information, here's a breakdown of the acceptance criteria and the study details:

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

The document does not explicitly state "acceptance criteria" for performance metrics like "Stimulating Velocity" or "Sucking Intensity" that would typically be defined as a required range the device must operate within to be considered safe and effective. Instead, it presents a comparison table of the subject device's specifications against a predicate device. The underlying "acceptance" is based on the device demonstrating performance within a range that is considered safe and effective for breast pumps, primarily by showing substantial equivalence to a predicate device.

The justification for "acceptance" is that "These differences do not raise different questions of safety or effectiveness."

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 focuses on non-clinical performance testing and does not report any clinical tests or studies involving a "test set" of patients or data in the way a diagnostic AI device would. The testing performed is primarily focused on engineering performance, electrical safety, EMC, biocompatibility, and software validation. Therefore, sample sizes for a test set of data or data provenance are not applicable in this context.

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)

Not applicable. As noted above, this submission does not involve clinical data or "ground truth" established by experts in the context of diagnostic performance. The validation is against established safety and performance standards, and engineering specifications.

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

Not applicable. There is no "test set" requiring adjudication by experts.

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 device is a breast pump, not a diagnostic device that would involve human readers or AI assistance in interpretation.

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

Not applicable. This device is a mechanical breast pump with software control, not an algorithm being evaluated for standalone performance. Its "performance" is its ability to express milk, which is assessed through engineering tests (pressure, cycle speed, etc.), not through an "algorithm-only" evaluation.

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

For this device, the "ground truth" for proving substantial equivalence relates to:

• Compliance with recognized performance standards (e.g., minimum and maximum pressure, cycle speed, sound volume).
• Compliance with safety standards (biocompatibility, electrical safety, EMC).
• Food and patient contact material assessments against regulatory criteria (FDA 21 CFR and ISO 10993).
• Software validation against FDA guidance.

The "ground truth" is therefore regulatory and engineering compliance, rather than a clinical ground truth like pathology or expert consensus on clinical outcomes.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a "training set" of data for algorithm development.

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

Not applicable. As there is no training set, there is no ground truth establishment for it.

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