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The EUROIMMUN IFA: Crithidia luciliae (anti-dsDNA) EUROPattern test kit is intended for the quantitative determination of human antibodies of immunoglobulin class IgG against anti-double stranded DNA (dsDNA) in human serum with the EUROPattern Microscope and Software automated instrument. It is used as an aid in the diagnosis of systemic lupus erythematosus (SLE), in conjunction with other laboratory and clinical findings. All suggested results obtained with the EUROPattern system must be confirmed by trained personnel.

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The provided text is a U.S. FDA 510(k) clearance letter for the EUROIMMUN IFA: Crithidia luciliae (anti-dsDNA) EUROPattern test kit. This document primarily focuses on regulatory approval and indications for use, stating that the device is substantially equivalent to legally marketed predicate devices.

However, the letter does NOT contain the detailed information about acceptance criteria or the specific study that proves the device meets those criteria, as typically found in a clinical study report or a more comprehensive submission document.

Therefore, I cannot provide the requested information from the provided text. The document does not include:

1. A table of acceptance criteria and reported device performance.
2. Sample sizes for test sets, data provenance, or details about training sets.
3. Information about experts, ground truth establishment, or adjudication methods.
4. Details about MRMC studies, effect sizes, or standalone algorithm performance.

To provide the requested information, you would typically need to refer to the device's 510(k) summary, the full 510(k) submission, or relevant clinical study publications, which are not included in this document.

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The M1026A Anesthesia Gas Module is intended to measure and monitor anesthesia gas contents in the ventilation circuitry of a patient and to provide this data to health care professionals in form readings, waves and alarms, via the Component Monitoring System, for the support of clinical decision making.

The device is indicated for use in health care facilities by health care professionals whenever there is a need for adult, pediatric and neonate patient anesthesia gas monitoring.

The above device operates with the HP Viridia Anesthesia Component Monitoring System (ACMS) and the HP Viridia 24 HP model 1204A through a digital interface (RS232). The monitoring system is known as the HP Model M1166A Component Monitoring System. When coupled with the above ACMS, the device will measure and display respiratory gases and anesthetic agents of ventilated patients. The device will signal physiological alarms and document deviations when preset limits are exceeded. An INOP ("inoperative") alarm is triggered and a message is displayed in the event of malfunction, lack of detectable breath, power disconnects, and other inoperative states.

The provided text doesn't contain detailed acceptance criteria or a specific study proving the device meets those criteria in a quantitative sense as might be expected for an AI/ML device. The document is a 510(k) summary for a medical device (Hewlett Packard M1026A Anesthetic Gas Monitor), focusing on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the typical AI/ML validation context.

However, based on the provided text, I can extract information related to the device's intended function and regulatory classification. I will infer "acceptance criteria" from the device's function and regulatory classification categories, and "reported device performance" from the statement of substantial equivalence to a predicate device that has already met regulatory standards.

Here's the information structured to best fit your request, with explicit notes about what information is not present in the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a conventional medical device (anesthetic gas monitor) and not an AI/ML diagnostic tool, the "acceptance criteria" within this 510(k) context are implicitly tied to its ability to accurately measure and monitor anesthetic gas contents, respiratory gases, and signal alarms, consistent with the performance of its predicate device. The "reported device performance" is primarily the assertion of substantial equivalence.

Note: The 510(k) summary emphasizes the substantial equivalence of the new device to a legally marketed predicate device (HP M1026A Option A01, K951127). For conventional devices like gas monitors, validation typically involves demonstrating that the new device meets performance specifications (e.g., accuracy, precision, response time) that are equivalent to or better than the predicate, often through bench testing and, in some cases, limited clinical validation if new indications or significant technological changes are introduced. This summary does not provide specific numerical performance metrics, test protocols, or raw data from such validation steps; it relies on the regulatory pathway of substantial equivalence.

Detailed Information as Requested (with caveats):

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

• Information Not Provided: The text does not detail any specific "test set" in the context of data used for algorithm validation. This device is a hardware monitoring system, and its approval is based on substantial equivalence to a predicate device, implying its performance validation (e.g., accuracy, reliability) was likely based on internal testing and comparison to the predicate's established performance, rather than a data-driven "test set" as seen with AI/ML.

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)

• Information Not Provided: As there is no "test set" in the context of an AI/ML algorithm requiring expert ground truth labeling mentioned, this information is not applicable and not present.

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

• Information Not Provided: Not applicable, as no algorithm test set is mentioned.

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

• Information Not Provided: This device is an anesthetic gas monitor, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study involving human "readers" and AI assistance is not relevant or described.

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

• Information Not Provided: Not applicable. This is a hardware device; no "algorithm only" performance is described in the context of an AI/ML system.

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

• Inferred based on device type: For a gas monitor, "ground truth" would typically refer to the "true" concentration of gases, established by highly accurate calibration gases or reference analytical methods. However, the document does not specify the exact methods or certifications for this, largely relying on the substantial equivalence and the use of commercially available, validated gas analysis modules.

8. The sample size for the training set

• Information Not Provided: Not applicable, as this is not an AI/ML device with a "training set."

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

• Information Not Provided: Not applicable, as this is not an AI/ML device with a "training set."

In summary, the provided document is a regulatory submission for a conventional medical device, focused on demonstrating substantial equivalence. It does not contain the specific performance study details, datasets, or expert review processes commonly described for AI/ML-based diagnostic devices. The "acceptance criteria" are implicitly met by demonstrating that the device functions as intended and is substantially equivalent to a previously cleared device.

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