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About twelve months ago, the general consensus seemed to be that OnLive (the poster-child for cloud gaming) faced a doubtful future. Yet today, the company is still alive and has now raised $56.5M, with the most recent investment coming from HTC. Their competitor, Gaikai, (who have a slightly different business model) has raised $15M from more high profile investors. Analysts seem to be making encouraging noises. But many game developers I know continue to shake their heads. What gives?
1. The Internet Unlike digital downloads, streaming games require both down AND upstream communications. Any latency is going to cause trouble, particularly for real-time games. Unfortunately, the Internet is a latency-filled place. This is why OnLive has had to raise so much capital (and possibly why Gaikai only streams demos): to provide a good consumer experience, cloud-gaming services need to operate in data centres which are physically close to the gamer. Although this is less of an issue for single-player games, overall it is not a business which scales well (from a capital perspective).
Days alive and at home (DAH) is a patient centered outcome measureable in routinely collected health data. The validity and minimally important difference (MID) in hip fracture have not been evaluated.
To assess predictive validity, we assessed the correlation between DAH90 and DAH365 using Spearman rank correlation (with this analysis limited to those who survived 90 days, as including those who did not survive to 90 days would upwardly bias the correlation coefficient estimate). Post-hoc, we calculated the correlation coefficient after subtracting the DAH90 value from the DAH365 value (as a high DAH90 value would upwardly bias the DAH365 value). Furthermore, for these individuals alive at the 90-day DAH ascertainment window, we calculated the ability of the quintile of DAH value in the 90-day period to discriminate subsequent mortality risk in the 365-day follow-up by calculating the c-statistic using Bayesian logistic regression. To test the impact of parameterising DAH90 as a quintile variable, we also repeated the predictive validity analysis with DAH90 as a restricted cubic spline with four knots.
The forest plot depicts the median adjusted difference in days alive at home after hip fracture admission and the associated 95% credible intervals based on the highest probability density interval from the posterior distribution, with results adjusted for patient-level factors.
The forest plot depicts the median adjusted difference in days alive at home after hip fracture admission and the associated 95% credible intervals based on the highest probability density interval from the posterior distribution, adjusted for admission-level and procedure-level factors in those with surgical fixation. ICU, intensive care unit.
The forest plot depicts the median adjusted difference in days alive at home in the 365 days after hip fracture admission and the associated 95% credible intervals (based on the highest probability density interval from the posterior distribution) among patients with hip fracture who survived 90 days after admission, with the quintile of days alive at home in 90 days as the predictor.
In this population-based cross-sectional analysis of linked health administrative data, we found that the number of days alive and at home after hip fracture admission had construct and predictive validity as a patient-centred outcome that can be captured from routinely collected electronic data sources. We further estimated that a difference of 11 days may represent an MID in DAH90 for patients with hip fracture, although this value may vary in certain clinically relevant subgroups and should be further evaluated with direct patient engagement. Overall, DAH90 may represent an important outcome for reporting, quality improvement and future registry-linked pragmatic trials in the growing population of older people with hip fractures. 2b1af7f3a8