Abstract: The present invention relates to a somatostatin analogue composition for radiopharmaceutical use, in particular for diagnostic or therapeutic use. More specifically the somatostatin analogue is a receptor-selective somatostatin peptide antagonist.

Abstract: Embodiments of the disclosure relate to an optical fiber cable having at least one optical fiber, a cable jacket, and a foam layer. The cable jacket has an inner surface and an outer surface. The outer surface is an outermost surface of the optical fiber cable, and the inner surface is disposed around the at least one optical fiber. The foam layer is disposed between the at least one optical fiber and the cable jacket. The foam layer includes a polymer component having from 30% to 100% by weight of a polyolefin elastomer (POE) or thermoplastic elastomer (TPE) and from 0% to 70% by weight of low density polyethylene (LDPE). The foam layer has a closed-cell morphology having pores with an average effective circle diameter of 10 ?m to 500 ?m. Further, the expansion ratio of the foam layer is at least 50%.

Abstract: To automatically model oncology treatment severity levels for a particular patient, a plurality of independently generated and potentially inconsistent observation data records are received for a patient. A condition-specific plurality of observation data records each having a common cancer type identifier are selected, and those condition-specific observation records are filtered to potentially eliminate one or more observation data records failing to satisfy one or more preliminary filter criteria. A specific pre-processing process for the remaining observation data records is executed to generate one or more output data records having at least one shared identifier, and the output data records are provided as model input to a machine-learning based severity model to generate a severity score for the patient.

Abstract: To enable automated processing of certain observation data by machine-learning based models, as well as to ensure those machine-learning based models are trained utilizing reliable and consistent data, independently generated observation data records each comprising biomarker mutation indicators, are utilized to generate a model input data set by applying biomarker mutation indicator-based filters, including intra-date filters and inter-date filters to identify and rectify observation data records that do not satisfy applicable biomarker mutation indicators. By identifying and rectifying the observation data records that do not satisfy applicable biomarker mutation indicators, a clean, model input data set is generated that is then utilized to generate a severity score for the patient.

Abstract: Anti-EGFR antibodies, therapeutic compositions comprising combinations of anti-EGFR antibodies, as well as methods for using such antibodies and compositions to treat EGFR-related disorders (e.g., cancers), are disclosed.

Abstract: Anti-EGFR antibodies, therapeutic compositions comprising combinations of anti-EGFR antibodies, as well as methods for using such antibodies and compositions to treat EGFR-related disorders (e.g., cancers), are disclosed.

Abstract: Anti-EGFR antibodies, therapeutic compositions comprising combinations of anti-EGFR antibodies, as well as methods for using such antibodies and compositions to treat EGFR-related disorders (e.g., cancers), are disclosed.

Abstract: A system and computer-implemented method for grouping medical records implements a multi-level analysis of the records. The level of analysis for each record is determined based upon the time proximity of each record to the defining medical procedure or service (anchor procedure) to be analyzed. Once an anchor procedure is identified, claim records are processed to determine whether any of the records should be grouped with the anchor procedure into a procedure episode group (PEG). First, the date of service for each claim record is identified to determine whether the claim record falls within time window. The claim records falling within the window then are assessed to determine whether each claim record is sufficiently related to the anchor procedure (for example, by determining whether the diagnostic, procedure, or episode treatment group coding of each claim record is associated with the anchor procedure).

Abstract: A system and computer-implemented method for grouping medical records implements a multi-level analysis of the records. The level of analysis is based upon the time proximity of each record to the defining medical procedure or service (anchor procedure) to be analyzed. Claim records are processed to determine whether any of the records should be grouped with the anchor procedure into a procedure episode group (PEG) using the date of service for each claim record and a level of relationship between the claim records and the anchor procedure. The requisite level of relationship between the claim records and the anchor procedure depends upon the position of the records within the time window. Claim records having the requisite relationship level associated with the portion of the time window in which they fall are included in the PEG.

Abstract: A system and computer-implemented method for grouping medical records implements a multi-level analysis of the records. The level of analysis for ach record is determined based upon the time proximity of each record to the defining medical procedure or service (anchor procedure) to be analyzed. Claim records are processed to determine whether any of the records should be grouped with the anchor procedure into a procedure episode group (PEG) using the date of service for each claim record and a level of relationship between the claim records and the anchor procedure. The requisite level of relationship between the claim records and the anchor procedure depends upon the position of the records within the time window. Claim records having the requisite relationship level associated with the portion of the time window in which they fall are included in the PEG.

Abstract: A system and computer-implemented method for grouping medical records implements a multi-level analysis of the records. The level of analysis for each record is determined based upon the time proximity of each record to the defining medical procedure or service (anchor procedure) to be analyzed. Once an anchor procedure is identified, claim records are processed to determine whether any of the records should be grouped with the anchor procedure into a procedure episode group (PEG). First, the date of service for each claim record is identified to determine whether the claim record falls within time window. The claim records falling within the window then are assessed to determine whether each claim record is sufficiently related to the anchor procedure (for example, by determining whether the diagnostic, procedure, or episode treatment group coding of each claim record is associated with the anchor procedure).

Abstract: Cyclic redundancy check processing is applied advantageously to a set of input data that includes an unknown data portion and a data portion that is already known before the unknown data portion becomes available. A syndrome contribution that the already-known data portion contributes to a syndrome for the set of input data is determined before the unknown data portion becomes available. When the unknown data portion becomes available, the syndrome for the set of input data is determined based on the unknown data portion and the syndrome contribution.

Abstract: Cyclic redundancy check (CRC) processing is applied to a received sequence of data blocks that are defined by respective sequences of sets of parallel data. For each data block, there is produced a sequence of syndromes that respectively correspond to the sets of parallel data within the data block. The final syndrome in the sequence of syndromes corresponds to all of the data in the data block. The time required for CRC processing can be reduced by concurrently producing first and second ones of the syndromes that respectively correspond to first and second ones of the sets that are respectively contained in first and second ones of the data blocks.

Abstract: A sequence of cyclic redundancy check syndromes can be produced based on a received sequence of sets of parallel data wherein different ones of the sets can have respectively different parallel data widths. Some of the syndromes are produced based on respectively corresponding ones of the sets that each have a first parallel data width. At least one of the syndromes is produced based on a corresponding at least one of the sets that has a second parallel data width that is less than the first parallel data width. The last syndrome of the sequence of syndromes corresponds to all of the data in the received sequence of sets.

Abstract: A system and computer-implemented method for grouping medical records implements a multi-level analysis of the records. The level of analysis for each record is determined based upon the time proximity of each record to the defining medical procedure or service (anchor procedure) to be analyzed. Once an anchor procedure is identified, claim records are processed to determine whether any of the records should be grouped with the anchor procedure into a procedure episode group (PEG). First, the date of service for each claim record is identified to determine whether the claim record falls within time window. The claim records falling within the window then are assessed to determine whether each claim record is sufficiently related to the anchor procedure (for example, by determining whether the diagnostic, procedure, or episode treatment group coding of each claim record is associated with the anchor procedure).

Abstract: In one aspect, an integrated circuit device including a first-level module configurable to receive and transmit control information, said first level module including a first sub-level module, a second sub-level module operably coupleable to the first sub-level module, and a third sub-level module operably coupleable to the second module; and a second-level module operably coupleable to the first-level module is disclosed.

Abstract: A system and computer-implemented method for grouping medical records implements a multi-level analysis of the records. The level of analysis for each record is determined based upon the time proximity of each record to the defining medical procedure or service (anchor procedure) to be analyzed. Once an anchor procedure is identified, claim records are processed to determine whether any of the records should be grouped with the anchor procedure into a procedure episode group (PEG). First, the date of service for each claim record is identified to determine whether the claim record falls within time window. The claim records falling within the window then are assessed to determine whether each claim record is sufficiently related to the anchor procedure (for example, by determining whether the diagnostic, procedure, or episode treatment group coding of each claim record is associated with the anchor procedure).

Abstract: In one aspect, an integrated circuit device including a first-level module configurable to receive and transmit control information, said first level module including a first sub-level module, a second sub-level module operably coupleable to the first sub-level module, and a third sub-level module operably coupleable to the second module; and a second-level module operably coupleable to the first-level module is disclosed.

Abstract: A sequence of cyclic redundancy check syndromes can be produced based on a received sequence of sets of parallel data wherein different ones of the sets can have respectively different parallel data widths. Some of the syndromes are produced based on respectively corresponding ones of the sets that each have a first parallel data width. At least one of the syndromes is produced based on a corresponding at least one of the sets that has a second parallel data width that is less than the first parallel data width. The last syndrome of the sequence of syndromes corresponds to all of the data in the received sequence of sets.

Abstract: Cyclic redundancy check (CRC) processing is applied to a received sequence of data blocks that are defined by respective sequences of sets of parallel data. For each data block, there is produced a sequence of syndromes that respectively correspond to the sets of parallel data within the data block. The final syndrome in the sequence of syndromes corresponds to all of the data in the data block. The time required for CRC processing can be reduced by concurrently producing first and second ones of the syndromes that respectively correspond to first and second ones of the sets that are respectively contained in first and second ones of the data blocks.