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The Drip Alert device is a passive device that measures time between intravenous drops and sounds an alarm when the time between drops falls outside an acceptable range due to air in the line, occlusion, low or empty fluid in the solution bag, high or low flow rate, and low battery.

The Drip Alert device is intended to be used as a supplementary monitor with a standard IV administration set such that an alarm sounds when the drip rate in the drip chamber of the administration set falls outside a preselected range of acceptable drip rate deviation. The deviation in the drip rate may be due to air in the IV line, occlusion, excessive movement by the patient or displacement of the IV catheter.

The Drip Alert device is a passive device that measures time between intravenous drops and sounds an alarm when the time between drops falls outside an acceptable range due to air in the line, occlusion, low or empty fluid in the solution bag, high or low flow rate, and low battery. The Drip Alert device does not have a pump or clamping mechanism. It uses a processor to perform calculations and measurements and has a flow meter. It is powered by 2-AAA batteries with a typical 30 day life. It is a Class II Device.

This document, a 510(k) summary for the Drip Alert intravenous drip monitor, provides limited information about specific acceptance criteria and performance studies. The focus of this submission is to demonstrate substantial equivalence to predicate devices rather than necessarily reporting on extensive performance studies with defined acceptance criteria.

Based on the provided text, here's what can be extracted and what remains unknown:

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

The document does not explicitly state quantitative acceptance criteria or specific performance metrics as they would typically be presented from a defined study. Instead, it provides a comparative chart showing features and capabilities against predicate devices. The "performance" is implicitly defined by its functionality as described in the "Indication for Use" and the features listed in the "Substantial Equivalence Chart."

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 510(k) summary focuses on equivalence to predicate devices rather than detailed performance study results from a test set.

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. As no specific performance study with a test set and ground truth is described, there's no mention of experts for establishing ground truth.

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

This information is not provided.

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

A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned and is not applicable here. The Drip Alert is a device (monitoring system), not an AI-assisted diagnostic tool that would involve human readers interpreting cases.

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

The Drip Alert is a standalone device in the sense that it performs its monitoring function automatically and signals an alarm when a deviation occurs. It is not an "algorithm only" in the context of AI/ML, but rather an electronic device with a processor that performs calculations and measurements. The document does not describe what would typically be considered a 'standalone performance study' in the context of recent medical device submissions, which often entail detailed metrics like sensitivity, specificity, etc. However, its core function is designed to operate without constant human intervention for its primary task of monitoring.

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

This information is not provided.

8. The sample size for the training set

This information is not provided. The Drip Alert is described as having a processor to perform calculations; however, the document does not suggest it utilizes complex machine learning algorithms that would require a "training set" in the modern sense. It appears to be a rule-based or threshold-based system.

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

This information is not provided and is likely not applicable given the apparent nature of the device (not a machine learning model requiring a training set with ground truth).

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