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The MectaLIF implants in combination with supplemental fixation are indicated for use with autogenous bone graft in patients with degenerative disc disease (DDD) at one or two contiguous spinal levels from L2 - S1 whose condition requires the use of interbody fusion. Degenerative disc disease is defined as discogenic pain with degeneration of the disc confirmed by patient history and radiographic studies. These patients may have had a previous non-fusion spiral surgery at the involved spinal level(s). Patients must be skeletally mature. Patients should have received 6 months of nonoperative treatment prior to treatment with the devices.

MectaLIF Transforaminal TiPEEK lumbar intervertebral body fusion device is characterized by different sizes of implants that can be applied with a TLIF procedure (Transforaminal Lumbar Intervertebral Fusion). They are provided sterile and used to replace a degenerative disc in order to restore the height of the spinal column structure. MectaLIF Transforaminal TiPEEK is intended to be used in combination with posterior fixation (e.g. Pedicle Screw System) as well as an autogenous bone graft. MectaLIF Transforaminal TiPEEK consists of a PEEK (ASTM F2026) body, tantalum (ISO 13782 / ASTM F560) markers and a titanium (Ti6A14V ISO 5832-3 / ASTM F136) gear that acts as an instrument interface. The implant surface is coated with commercially pure titanium (CPT) ASTM F1580).

This document describes a 510(k) premarket notification for the "MectaLIF Transforaminal TiPEEK" intervertebral body fusion device. The focus of a 510(k) submission is to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving efficacy through clinical studies for new device types. As such, the information provided primarily addresses non-clinical performance and material characterization, and explicitly states that no clinical studies were conducted. Therefore, the request for details related to acceptance criteria, AI performance, expert adjudication, and MRMC studies are not applicable to this specific submission.

Here's a breakdown of the available information regarding testing and "acceptance" in the context of this 510(k):

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

Since this is a 510(k) for a device with a new material composition (TiPEEK coating) but otherwise similar design to a predicate, the "acceptance criteria" are primarily related to material properties, biocompatibility, and mechanical performance to demonstrate equivalence. The document doesn't provide a direct table of acceptance criteria and results in the typical sense of a clinical trial. Instead, it lists the types of non-clinical tests performed:

• Pyrogen test according to USP chapter for pyrogenicity determination.
• Note: The subject devices are not labeled as non-pyrogenic or pyrogen free. |
  | Biocompatibility (implicit) | - PEEK (ASTM F2026), tantalum (ISO 13782 / ASTM F560), titanium (Ti6A14V ISO 5832-3 / ASTM F136), commercially pure titanium (CPT) ASTM F1580 coatings are listed, indicating adherence to established material standards. The submission states the TiPEEK coating is the same as cleared in K133192. |
  | Technological Characteristics Comparison | - Claimed equivalence in shape, sizes, biocompatibility, device usage, sterility, shelf life, and packaging to the predicate device (K131671). The only difference is the material (TiPEEK coating). |

The acceptance criteria for these tests would typically be compliance with the specified standards (e.g., ASTM, ISO, USP) and demonstrating that the new material/device performs equivalently to, or within acceptable safety parameters of, the predicate device. The document states:
"The comparison of technological characteristics and performance data provided within this submission, shows that there are no new risks associated with the subject devices design, and supports the substantial equivalence of the MectaLIF Transforaminal TiPEEK implants to the identified predicate devices."

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

• Sample Size for Test Set: Not explicitly stated in terms of the number of devices tested for wear or pyrogenicity, but testing was conducted on samples of the device. This is typical for engineering and material characterization studies rather than clinical trials on human subjects.
• Data Provenance: The test methods refer to international and US standards (IL, European Pharmacopoeia, USP, ASTM, ISO). The company, Medacta International SA, is located in Switzerland, and Medacta USA in Memphis, Tennessee. The specific location where the non-clinical tests were conducted is not detailed, but it would have been laboratories certified to perform these types of material and mechanical tests. This data is non-clinical/bench testing.

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. This is a non-clinical submission for a medical device's physical and material properties. There is no "ground truth" derived from expert clinical assessment for this type of submission. The "ground truth" is established by adherence to validated test methods and material specifications.

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

Not applicable. As this is not a clinical study involving human assessment of images or outcomes, adjudication methods are not relevant.

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 an implantable intervertebral body fusion device, not an AI-powered diagnostic or assistive tool. No MRMC studies were performed.

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

Not applicable. This is not an algorithm or AI device.

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

For non-clinical testing of a medical implant, the "ground truth" is defined by established engineering and material science standards, performance specifications, and predicate device performance. For example, for a wear test, the ground truth is that the wear rate must be below a certain validated threshold, or comparable to the predicate device, as determined by the specific wear test protocol. For pyrogenicity, the ground truth is an absence of pyrogens per the specified USP/EP methods.

8. The sample size for the training set

Not applicable. This is not an AI/machine learning device.

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

Not applicable. This is not an AI/machine learning device.

In summary: This 510(k) submission for the MectaLIF Transforaminal TiPEEK device relies on non-clinical performance data and a comparison of technological characteristics to demonstrate substantial equivalence to a predicate device. It explicitly states that "No clinical studies were conducted," and therefore, none of the questions related to clinical performance, AI, expert assessment, or human-in-the-loop studies are relevant to this specific FDA submission document. The "proving the device meets acceptance criteria" here revolves around documented compliance with engineering standards and demonstration of equivalent physical and material properties to a previously cleared device.

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MySpine S2-Alar/Alar-Iliac is intended for use with M.U.S.T. Pedicle Screw System and its cleared indications for use. MySpine S2-Alar/Alar-Iliac guides (referred to from this point on as, MySpine guides) are intended to be used as anatomical perforating guides, specific to a patient's anatomy, to assist intra-operatively in the preparation of the screw trajectory in S1, S2 and in the Ilium. The guides are created using a surgical planning software which pre-operatively plans the positions of the components based upon radiological images of the patients' anatomical landmarks and the surgical equipment selected. MySpine guides are intended for single use only.

The MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides are a line extension to Medacta's MySpine Pedicle Screw Placement Guides. Identical to the other Medacta MySpine products, the MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides are a patient matched, pedicle targeted, technology involving the production of patient specific guides for placement of the M.U.S.T. Pedicle Screw System (K12115, K132878, K141988, K153664, K162061, and K171170). Specifically, the subject MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides are intended to be used as anatomical perforating guides to assist intra-operatively in the preparation of the screw trajectory in S1, S2 and in the Ilium. The MySpine software platform allows the surgeon to complete 3D pre-operative planning based on the patient's spinal CT scans. CT images are used to create a 3D model of the vertebrae that will represent the template used to generate the corresponding MySpine Screw Placement Guides fitting the patient's vertebral anatomy. The MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides as well as their bone models are single-use and they can be provided in sterile or non-sterile version.

The provided text describes a medical device, the "MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides," and its substantial equivalence determination by the FDA. However, the document does NOT contain information about specific acceptance criteria for a device's performance (like sensitivity, specificity, accuracy, etc.) nor does it usually detail specific studies that prove the device meets such criteria in terms of quantitative metrics suitable for the requested table.

Instead, this document focuses on demonstrating substantial equivalence to a predicate device based on similar technological characteristics (manufacturing process, material, biocompatibility, device usage, sterility, shelf life, packaging) and performance data from non-clinical studies (software validation, cadaver testing, guide accuracy, stability assessment). The "guide accuracy" mentioned is a general category and not a specific set of acceptance criteria with reported performance.

Therefore, I cannot fill in the table of acceptance criteria and reported device performance, nor can I answer questions related to sample size, expert qualifications, or ground truth establishment for a standalone algorithm performance study, because this information is not present in the provided text. The document explicitly states: "No clinical studies were conducted." and it does not describe an AI algorithm with human-in-the-loop performance.

Here's an overview of what can be extracted or inferred based on the provided text, and where information is missing:

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

• Acceptance Criteria: Not explicitly stated with quantifiable metrics (e.g., "accuracy must be > 95%"). The document talks about "software validation," "cadaver testing," "guide accuracy," and "stability assessment" as general categories of testing performed, aiming to show safety and effectiveness comparable to the predicate.
• Reported Device Performance: No specific quantitative performance metrics (e.g., accuracy percentages, error margins) are reported in the document.

Therefore, this table cannot be filled based on the provided input.

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

• Test Set Sample Size: Not specified. The document mentions "Cadaver testing" and "Guide accuracy" testing, but no details on the number of cadavers or cases used for these tests are provided.
• Data Provenance: Not specified.

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 no detailed test set and ground truth establishment methodology is described in the provided text. The document refers to "surgical planning software" that "pre-operatively plans the positions of the components based upon radiological images," but this is part of the device's function, not a ground truth establishment for an AI algorithm's performance evaluation.

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

• Not applicable as no detailed test set and ground truth establishment methodology is described.

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

• MRMC Study: No. The document explicitly states: "No clinical studies were conducted." The device (surgical guides) assists surgeons, but the documentation does not describe a study involving human readers and AI assistance.

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

• The device is a physical surgical guide produced using software, not a standalone AI algorithm that provides diagnostic or prognostic outputs. The "MySpine software platform" is integral to creating the guides. "Software validation" was performed, but no standalone algorithm performance as typically understood in AI/ML context (e.g., measuring diagnostic accuracy against ground truth without human intervention) is described.

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

• Not explicitly stated for any performance evaluation in terms of AI. For the device itself and its intended use, the anatomical landmarks from patient CT scans and selected surgical equipment (per planning software) would form the basis for the design of the patient-specific guides. The "guide accuracy" testing would presumably involve comparing the guide's output (screw trajectory) against the planned trajectory or anatomical reality, but the specific ground truth methodology for this testing is not detailed.

8. The sample size for the training set

• Not applicable. The document describes a medical device (surgical guides) and the software that creates them for individual patients. It does not refer to a machine learning model that requires a "training set" in the conventional sense for AI performance. The software uses patient-specific CT data for planning.

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

• Not applicable, as there is no mention of a "training set" for an AI model.

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The Reverse Shoulder Prosthesis is indicated for treatment of humeral fractures and for primary or revision total shoulder replacement in patients with a grossly deficient rotator cuff shoulder joint with severe arthropathy failed joint replacement with a grossly rotator cuff deficient shoulder joint.

The patient's joint must be anatomically suited to receive the selected implant(s), and a functional deltoid muscle is necessary to use the device.

The glenoid baseplate is intended for cementless application with the addition of screws for fixation.

The Lateralized Glenosphere are line extensions to the Medacta Shoulder Reverse System (K170452) and are compatible with the other Medacta cleared products Threaded Glenoid Baseplate (K171058), Glenoid Polyaxial Non-Locking Screws (K181826) and the Short Humeral Stem (K180089).

The Lateralized Glenosphere implants subject of this submission are comprised of the following products (9 sizes):

• Size Ø 32: to be coupled with Medacta Glenoid Baseplate Ø 22 or Ø 24.5mm
• Size Ø 36: to be coupled with Medacta Glenoid Baseplate Ø 22 or Ø 24.5 or Ø27mm
• Size Ø 39: to be coupled with Medacta Glenoid Baseplate Ø 24.5 or Ø27mm
• Size Ø 42: to be coupled with Medacta Glenoid Baseplate Ø 24.5 or Ø27mm

The Lateralized Glenosphere implants are part of the Medacta Shoulder Reverse System. The Medacta Shoulder Reverse System consists of the following components:

• Humeral Diaphysis Cemented;
• Humeral Diaphysis - Cementless;
• Humeral Reverse Metaphysis;
• Humeral Reverse HC Liner (also referred to as PE Liner);
• Glenoid Baseplate - Pegged;
• Glenoid Baseplate Threaded;
• Glenosphere;
• Glenoid Polyaxial Locking Screw;
• Glenoid Polyaxial Non-Locking Screw;
• Reverse Metaphysis Screw; and
• Glenosphere Screw.

The glenosphere is attached to the glenoid baseplate and secured by means of a taper connection and a fastening screw.

The purpose of the current submission is to gain clearance for the Lateralized Glenospheres, whose center is more lateralized respect to Medacta predicate device Glenosphere (Medacta Shoulder Reverse System - K170452).

The new option of lateralization allows the surgeon to intraoperatively select the desired level of ROM and resulting joint tension based on the patient's anatomy.

The Lateralized Glenosphere is made of CoCrMo ISO 5832-12 (Second Edition 2007-05-01) Implants For Surgery - Part 12: Wrought Cobalt- Chromium-Molybdenum Alloy [Including: Technical Corrigendum 1 (2008)], while the Glenosphere screw packed with the implant is made of Ti alloy (Ti-6A1-4V), enhanced with Type-II anodization, according to ISO 5832-3:2016 Implants For Surgery -Metallic Materials - Part 3: Wrought Titanium 6-Aluminum 4-Vanadium Alloy.

The provided text describes a 510(k) premarket notification for a medical device called the "Lateralized Glenosphere." This submission focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than proving the device meets specific performance criteria through a study with acceptance criteria in the typical sense of a diagnostic or predictive AI device.

Therefore, the requested information cannot be fully extracted as there is no study that proves the device meets specific acceptance criteria in the context of a diagnostic AI product, because this is a physical implant. The performance data section refers to mechanical tests on the implant itself, not a study of a diagnostic algorithm.

Here's an analysis of what information can be provided based on the input:

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

Based on the document, there isn't a table of acceptance criteria and reported device performance in the way one would describe for a diagnostic AI device (e.g., sensitivity, specificity, accuracy). Instead, there are mechanical tests performed with acceptance criteria based on established standards for shoulder prostheses.

2. Sample size used for the test set and the data provenance:

• Sample Size for Test Set: Not applicable in the context of a clinical test set for diagnostic accuracy. The "test set" here refers to the physical samples of the medical device (Lateralized Glenosphere and associated components) that underwent mechanical testing. The exact number of physical devices or components tested for each mechanical study is not specified in this summary.
• Data Provenance: The mechanical tests were conducted in a laboratory setting according to written protocols. There's no information about the country of origin or whether it was retrospective/prospective as it relates to patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. This device is a physical implant, not a diagnostic AI device requiring expert-established ground truth for a test set. The "ground truth" for the mechanical tests would be the established engineering standards (ASTM, ISO, European Pharmacopoeia, USP) and the physical properties observed during testing.

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

• Not applicable. Adjudication methods like 2+1 are used for establishing ground truth in diagnostic studies, not for mechanical testing of physical implants. The "adjudication" for mechanical tests is agreement with the predefined acceptance criteria of the standards.

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 not an AI diagnostic device. No human reader studies (MRMC) were conducted as the device is a physical implant.

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

• Not applicable. This is not an AI algorithm. The performance data refers to the mechanical integrity and biological safety of the physical medical device itself.

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

• The "ground truth" for the performance data is based on recognized engineering standards (ASTM F2028-17, ASTM F1378-17), pharmacopoeia guidelines (European Pharmacopoeia §2.6.14, USP , USP ), and design validation reports for the mechanical and biocompatibility aspects of the implant.

8. The sample size for the training set:

• Not applicable. This device is a physical implant, not an AI model that requires a training set.

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

• Not applicable. As there is no AI model or training set, this question is not relevant.

In summary: The provided document is a 510(k) summary for a physical medical implant. The "acceptance criteria" and "performance data" refer to mechanical and biocompatibility testing against established engineering and medical device standards, not to the performance of a diagnostic or AI-driven system. Therefore, most of the requested information, which is typically relevant for AI/diagnostic device evaluation, is not applicable or provided in this context.

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The NextAR™ TKA Platform is intended to be used to support the surgeon during total knee replacement procedures by providing information on bone resections, ligaments behavior, instrument and implant positioning.

The NextAR™ TKA Platform is intended to be used in combination with NextARTM stereotaxic instruments, the MyKnee® NextAR™ cutting guides, and general surgical instruments to implant the GMK Sphere Total knee system and perform ligament balancing. As an optional display, the smart glasses can be used auxiliary to the NextAR Platform to view the same 2D stereotaxic information as presented by the NextAR Platform.

The smart glasses should not be relied upon solely and should always be used in conjunction with the primary computer display.

The MyKnee® NextAR cutting guides include a camera/target holder and a PSI MyKnee® cutting guide both for tibia and femur. The MyKnee® cutting guides must be used as anatomical cutting blocks specific for a single patient anatomy, to assist in the positioning of total knee replacement components intraoperatively and in guiding the marking of bone before cutting. MyKnee® NextAR TKA cutting guides are for single use only.

The NextAR™ stereotaxic instruments are intended to be used to surgeon during specific orthopedic surgical procedures by providing information on bone resections, ligaments behavior, instrument and implant positioning. The NextAR™ stereotaxic instruments, when registered with the myKnee NextAR TKA cutting guides, provide reference to a patient's rigid anatomical structures, such as the femur and tibia, that can be identified relative to pre-operative CT based planning.

The NextAR™ TKA Platform is a CT based computer-assisted surgical navigation platform used in total knee replacement surgery and includes the following components:

• navigation software which displays information to the surgeon in real-time;
• Augmented Reality glasses;
• optical tracking system;
• PC based hardware platform;
• MyKnee NextAR Cutting Blocks; and
• reusable surgical instruments for total knee replacement procedures.

The system operates on the common principle of stereotaxic technology in which passive markers are mounted on the bones and an infrared camera is used to monitor the spatial location of the markers to avoid intraoperative registration of bony landmarks. Tracking sensors attached to the bones enable the surgeon to view the position and orientation of bones and instrumentation relative to preoperative data in real-time while performing the surgical procedure. The tracking sensors are provided sterile.

The NextAR™ TKA Platform aids the surgeon in executing the surgical plan by visualizing all the information in real time in a screen monitor. The placement of the implants is performed by cutting the bones using MyKnee® NextAR™ Cutting Blocks while reusable surgical instrumentation (provided non-sterile) guided by the tracking sensors can be used for recut. Although the position of the implants can be validated to assess the correct execution of the planning, the surgeon can change the surgical plan intraoperatively by analyzing the 3D models of the patient, the CT scan, and the 3D geometry of the implants.

The MyKnee® NextAR™ Cutting Blocks, manufactured from medical grade nylon, are single use patient-specific blocks which are designed from patient MRI or CT images. The blocks are designed to be used in standard medial or lateral parapatellar surgical approaches with each set comprised of a femoral block, a tibial block, and two bone models of the patient's femur and tibia (optional). The femoral cutting blocks are provided in right and left configurations in sizes 1 to 7 and 1+ to 6+ and the tibial cutting blocks are provided in right and left configurations in sizes 1 to 6. The blocks are provided sterile via gamma irradiation or non-sterile.

The FDA 510(k) summary for the NextAR™ TKA Platform (K193559) provides details on the device's acceptance criteria and the studies conducted to prove its performance.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a single, consolidated format. However, it mentions various types of testing and states that "Testing was conducted according to written protocols with acceptance criteria that were based on standards." The "Discussion" section also states, "Minor differences in the optical tracking systems and use of patient specific instrumentation are addressed by performance testing." This implies that the performance testing confirmed the device met established criteria for those specific aspects.

Based on the information provided, we can infer some of the areas where acceptance criteria would have been applied:

2. Sample Size for Test Set and Data Provenance

The document explicitly mentions a "cadaver study" as part of the performance data.

• Sample Size for the Test Set: Not explicitly stated in the provided text.
• Data Provenance: The cadaver study would involve human remains, but the exact country of origin or whether it was retrospective/prospective in the context of data collection for this specific study is not detailed. However, cadaver studies are inherently prospective for the purpose of testing the device.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly mention the number of experts, their qualifications, or their role in establishing ground truth specifically for the reported studies (e.g., cadaver study). For a device like this, ground truth would likely be established through precise anatomical measurements or post-hoc imaging.

4. Adjudication Method for the Test Set

The document does not detail any adjudication method (e.g., 2+1, 3+1) for the test set. Adjudication methods are typically associated with human interpretation of medical images or data, which is not the primary focus for establishing the technical accuracy of a navigation system in a cadaver study.

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

No mention of an MRMC comparative effectiveness study, or the effect size of human readers improving with AI vs. without AI assistance, is made. This device is a surgical navigation platform, not an AI-assisted diagnostic tool that heavily relies on human reader interpretation of images. The smart glasses are described as an auxiliary display, not an AI-driven interpretive aid for human readers.

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

The performance testing, particularly the "performance testing to evaluate mechanical and optical properties" and elements of the "cadaver study," would inherently involve evaluating the standalone performance of the algorithm and system components. The software validates the navigation software's ability to display information to the surgeon accurately. The cadaver study would assess how accurately the system guides the surgeon, implying that the system's output (measurements, guidance) is evaluated independently of a human's ultimate judgment during the assessment phase of the study, though a human surgeon performs the actions based on the guidance.

7. Type of Ground Truth Used

For the specific performance testing of this surgical navigation platform, especially in the cadaver study, the ground truth would likely be established using precise direct physical measurements (e.g., with highly accurate measurement tools or CMMs) or potentially post-operative imaging with precise measurements of the cadaver bones to verify the accuracy of resections and implant positioning as guided by the system. The "pre-operative CT based planning" is used by the device, and the device ensures "reference to a patient's rigid anatomical structures... that can be identified relative to pre-operative CT based planning." Therefore, the ground truth would be tied back to the anatomical accuracy relative to these plans.

8. Sample Size for the Training Set

The document does not provide information about a specific "training set" sample size. This type of device relies on established biomechanical principles and image processing of CT scans for its planning and navigation, rather than a deep learning model that requires a discrete training set in the conventional sense. The software is validated, but not "trained" in an iterative machine learning fashion on a large dataset of patient outcomes.

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

As no specific training set for a machine learning model is mentioned, the method for establishing its ground truth is not applicable or detailed in this summary. The system's foundational data (e.g., anatomical models, instrument specifications) are established through engineering design, scientific principles, and preclinical testing.

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