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510(k) Data Aggregation
(39 days)
BIOTEQUE SCALP VEIN SET
BIOTEQ® SCALP VEIN SET act as a pathway to administer parenteral fluid/ medication into the patient's vascular system.
BIOTEQ® SCALP VEIN SET (BS-120 (22G); BS-121(23G) ; BS-122(24G))consist of the following 5 major components: Protective cap for needle, the stainless needle, the plastic butterfly wing , the PVC Tube, the PVC safety Cap. These 5 major components assembled together as SCALE VEIN SET for use as a pathway to administer medical fluid from extracorporeal system through the device into venous blood.
The provided text is a 510(k) summary for the BIOTEQ® SCALP VEIN SET. It describes the device, its intended use, and its substantial equivalence to predicate devices. However, it does not include detailed information regarding a study that proves the device meets specific acceptance criteria in the way a clinical trial or performance study for a diagnostic AI device would.
Instead, the submission relies on demonstrating substantial equivalence to already approved predicate devices through bench testing that confirms physical, chemical, biological, and sterilization specifications.
Therefore, many of the requested categories (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance) are not applicable or cannot be extracted from this document, as this is not the type of study described.
Here's a breakdown of the information that is available based on your request:
Acceptance Criteria and Reported Device Performance
Acceptance Criteria Category | Reported Device Performance (as stated or implied) |
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Physical Specification | Conforms to applicable standards (not explicitly detailed) |
Chemical Specification | Conforms to applicable standards (not explicitly detailed) |
Biological Specification | Conforms to applicable standards, including ISO 10993 series & USP Pyrogenic standards |
Sterilization Specification | Conforms to applicable standards, including ISO 11607-1, ISO 11135 |
Intended Use | Serves as a pathway to administer parenteral fluid/medication into the patient's vascular system. |
Technological Characteristics | Similar to predicate devices; bench testing shows no new safety/effectiveness questions. |
Study Details (Based on available information)
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Sample size used for the test set and the data provenance: Not explicitly stated as a "test set" in the context of an AI/diagnostic device. The "bench testing" likely refers to laboratory-based evaluations of the device's components and assembled product against engineering and safety standards. The provenance (e.g., country of origin, retrospective/prospective) for this bench testing is not specified.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. For a device like a scalp vein set, "ground truth" typically relates to objective measurements against established engineering and safety standards, rather than expert interpretation of data.
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Adjudication method for the test set: Not applicable. No human interpretation or adjudication process is described for this type of bench testing.
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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 document pertains to a medical device (scalp vein set), not an AI diagnostic tool involving human readers.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical medical device, not an algorithm.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for this device's performance is based on established engineering standards, material specifications (e.g., ISO, USP), and functional performance requirements for medical devices of this type. It's objective, measurable criteria rather than subjective expert consensus or clinical outcomes data in the typical sense.
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The sample size for the training set: Not applicable. This is not a machine learning device, so there is no training set.
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How the ground truth for the training set was established: Not applicable, as there is no training set.
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