In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

[Q]: Context: Trauma centers are currently benchmarked on mortality outcomes alone. However, pay-for-performance measures may financially penalize centers based on complications. Our objective was to determine whether the results would be similar to the current standard method of mortality-based benchmarking if trauma centers were profiled on complications.', 'We analyzed data from the National Trauma Data Bank from 2007 to 2010. Patients 16 years or older with blunt or penetrating injuries and an Injury Severity Score (ISS) of 9 or higher were included. Risk-adjusted observed-to-expected (O/E) mortality ratios for each center were generated and used to rank each facility as high, average, or low performing. We similarly ranked facilities on O/E morbidity ratios defined as occurrence of any major complication. Concordance between hospital performance rankings was evaluated using a weighted κ statistic. Correlation between morbidity- and mortality-based O/E ratios was assessed using Pearson coefficients. Sensitivity analyses were performed to mitigate the competing risk of death for the morbidity analyses.', 'A total of 449,743 patients from 248 facilities were analyzed. The unadjusted morbidity and mortality rates were 10.0% and 6.9%, respectively. No correlation was found between morbidity- and mortality-based O/E ratios (r = -0.01). Only 40% of the centers had similar performance rankings for both mortality and morbidity. Of the 31 high performers for mortality, only 11 centers were also high performers for morbidity. A total of 78 centers were ranked as average, and 11 ranked as low performers on both outcomes. Comparison of hospital performance status using mortality and morbidity outcomes demonstrated poor concordance (weighted κ = 0.03, p = 0.22).\Question: Does benchmarking trauma centers on mortality alone reflect quality of care : implications for pay-for-performance?
[A]: Mortality-based external benchmarking does not identify centers with high complication rates. This creates a dichotomy between current trauma center profiling standards and measures used for pay-for-performance. A benchmarking mechanism that reflects all measures of quality is needed.


[Q]: Context: Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host.', 'Intra-host variants of the HCV E1/E2 region were extensively sampled from 58 chronically infected patients. After NGS error correction, the average number of reads and variants obtained from each sample were 3202 and 464, respectively. The distance between each pair of variants was calculated and networks were created for each patient, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. The work focused on large components having > 5% of all reads, which in average account for 93.7% of all reads found in a patient.\Question: Does next-generation sequencing reveal large connected networks of intra-host HCV variants?
[A]: Most intra-host variants are organized into distinct single-mutation components that are: well separated from each other, represent genetic distances between viral variants, robust to sampling, reproducible and likely seeded during transmission events. Facilitated by NGS, large components offer a novel evolutionary framework for genetic analysis of intra-host viral populations and understanding transmission, immune escape and drug resistance.


[Q]: Context: Given the modest responses to everolimus, a mTOR inhibitor, in multiple tumor types, there is a pressing need to identify predictive biomarkers for this drug. Using targeted ultra-deep sequencing, we aimed to explore genomic alterations that confer extreme sensitivity to everolimus.', 'We collected formalin-fixed paraffin-embedded tumor/normal pairs from 39 patients (22 with exceptional clinical benefit, 17 with no clinical benefit) who were treated with everolimus across various tumor types (13 gastric cancers, 15 renal cell carcinomas, 2 thyroid cancers, 2 head and neck cancer, and 7 sarcomas). Ion AmpliSeqTM Comprehensive Cancer Panel was used to identify alterations across all exons of 409 target genes. Tumors were sequenced to a median coverage of 552x. Cancer genomes are characterized by 219 somatic single-nucleotide variants (181 missense, 9 nonsense, 7 splice-site) and 22 frameshift insertions/deletions, with a median of 2.1 mutations per Mb (0 to 12.4 mutations per Mb). Overall, genomic alterations with activating effect on mTOR signaling were identified in 10 of 22 (45%) patients with clinical benefit and these include MTOR, TSC1, TSC2, NF1, PIK3CA and PIK3CG mutations. Recurrently mutated genes in chromatin remodeling genes (BAP1; n = 2, 12%) and receptor tyrosine kinase signaling (FGFR4; n = 2, 12%) were noted only in patients without clinical benefit.\Question: Does next-generation sequencing reveal somatic mutations that confer exceptional response to everolimus?
[A]:
Regardless of different cancer types, mTOR-pathway-activating mutations confer sensitivity to everolimus. Targeted sequencing of mTOR pathway genes facilitates identification of potential candidates for mTOR inhibitors.