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The Varian High Energy Linear Accelerator is intended to provide stereotactic radiosurgery and precision radiotherapy for lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.
The Varian High Energy Linear Accelerator is indicated for stereotactic radiosurgery and precision radiotherapy for lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

The Varian High Energy Linear Accelerator models provide various selections among the features, specifications, and accessories that have been most recently cleared as the Varian High Energy Linear Accelerator, K100890.
The changes to the Varian High Energy Linear Accelerator provide support for treatments using the high intensity photon treatment mode, also known as FFF (Flattening Filter Free). Additionally, couch motion rules are modified to enable the use of patient support systems where the targeted anatomy and the treatment isocenter area below the surface of the couch top. Modifications that augment existing safety controls are also included.
All other features of the Varian High Energy Linear Accelerator models remain as cleared by K100890.

Here's a summary of the acceptance criteria and study information for the Varian High Energy Linear Accelerator, based on the provided 510(k) summary:

The 510(k) K112839 describes modifications to the Varian High Energy Linear Accelerator, primarily to support high-intensity photon treatment mode (FFF - Flattening Filter Free) and updated couch motion rules. The submission asserts substantial equivalence to its predicate device, K100890.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the functional and safety requirements, as well as compliance with applicable international standards. The performance is reported by demonstrating that the modified device functions equivalently or with improved safety features compared to the predicate device.

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

The document does not specify a "test set" in the context of clinical data or patient samples. The testing described is primarily bench testing for verification and validation of functional requirements, use cases, hazard mitigation, and compliance with international standards. Therefore, the concept of sample size and data provenance (country, retrospective/prospective) related to patient data is not applicable here.

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

Not applicable. This submission focuses on engineering and regulatory compliance testing of a medical device, not on diagnostic accuracy requiring expert interpretation of medical images or patient outcomes for a "ground truth" establishment.

4. Adjudication Method for the Test Set

Not applicable. As described above, the testing involved engineering verification and validation against pre-defined specifications and standards, not clinical adjudication of diagnostic or treatment outcomes by experts.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is typically used for AI-powered diagnostic devices to assess the impact of AI assistance on human reader performance. The Varian High Energy Linear Accelerator is a treatment delivery device, and the changes described relate to its operational capabilities and safety features, not to diagnostic image interpretation or AI assistance for human readers.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

Not applicable. The device is a linear accelerator for radiation therapy, not a standalone AI algorithm. Its operation inherently involves human-in-the-loop (e.g., medical physicists, radiation oncologists, radiation therapists) for treatment planning and delivery. The "standalone" concept for an algorithm does not fit the nature of this device.

7. Type of Ground Truth Used

The "ground truth" for the performance testing effectively consisted of:

• Pre-defined specifications and functional requirements for the device.
• Hazard mitigation requirements based on risk analysis.
• Applicable international medical device standards (e.g., IEC 60601-1, IEC 60601-1-2, IEC 60601-2-1).

The testing aimed to demonstrate that the device met these engineering and safety benchmarks, rather than clinical outcomes or pathological diagnoses.

8. Sample Size for the Training Set

Not applicable. The Varian High Energy Linear Accelerator is a hardware and software system for radiation therapy delivery. It does not utilize a "training set" in the machine learning sense for its operational functions as described in this submission. Its software development and configuration are based on established engineering principles and requirements, not statistical learning from a large dataset.

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

Not applicable, as there is no "training set" in the context of machine learning for this device. The software and hardware capabilities are developed and validated against engineering specifications and regulatory standards.

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The Barcode Conical Collimator Verification device assists operators of radiation therapy devices by preventing irradiation until the conical collimator required by the treatment plan is in place.

The Barcode Conical Collimator Verification (BCCV) device provides verification of conical collimators for the Varian High Energy Clinac (K100890) and TrueBeam (K092871) devices. Conical collimators are used during stereotactic radiodurgery (SRS) treatments to deliver small diameter treatment beams. The BCCV will prevent treatment until the conical collimator, as required by the treatment plan, has been scanned.

The provided text describes a medical device, the Barcode Conical Collimator Verification (BCCV) system, and its FDA 510(k) clearance. However, it does not contain information about acceptance criteria, the study details (sample size, data provenance, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, or standalone performance), or training set details.

The document is a 510(k) summary for premarket notification, which primarily focuses on establishing substantial equivalence to a predicate device rather than providing detailed clinical efficacy or performance study results against specific acceptance criteria.

Therefore, I cannot fulfill your request for:

• A table of acceptance criteria and the reported device performance: This information is not present. The document only describes the device's function ("prevents treatment until the conical collimator, as required by the treatment plan, has been scanned"). It doesn't quantify performance metrics like accuracy, reliability percentages, or failure rates.
• Sample size used for the test set and the data provenance: Not mentioned.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
• Adjudication method for the test set: Not mentioned.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not mentioned. This device is an automated verification system, not an AI-assisted diagnostic tool for human readers.
• If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The device's function implicitly describes a standalone automated verification process, but no specific study details are provided.
• The type of ground truth used: Not mentioned.
• The sample size for the training set: Not mentioned.
• How the ground truth for the training set was established: Not mentioned.

In summary, the provided document is a regulatory submission for device clearance and does not contain the detailed performance study information you've requested. Such information would typically be found in performance data sections of the full 510(k) submission, which is not publicly available in this excerpt, or in separate scientific publications or internal validation reports.

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