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Patients lost to follow-up varied across studies. Two studies showed a low risk of bias in this domain: one of them reported one loss in the control group, which constituted 2.5% of the sample ( Bandolino Gomeisa u1bWH
); whereas the other study reported 11% and 10% losses to follow-up in the experimental and control groups, respectively, for similar reasons in both groups ( Steve Madden Rosanda iBLxJ6
). The two other studies showed a high risk of bias in this regard as one of them had different loss rates between the groups (9.2% in the experimental group and 3.7% in the control group) ( Fukahori 1999 ), while the other reported high rates of losses in both groups (43% in the experimental group and 26% in the control group) ( HavaianasSlim Paisage Flip Flops bRQA397
). None of the four studies reported an intention-to-treat analysis.

Three of the included studies showed a low risk of bias in this respect as they all described the results obtained for each of the outcomes included in the objectives or the methods section ( Fukahori 1999 ; Mendivil 2006 ; Nishijima 2007 ). The other study had a high risk of bias because it presented the results as differences between the initial and final values ( Kenneth Cole Reaction Lash Dance z9DU4Pd9

We could not conduct a meta-analysis due to the clinical heterogeneity, with different interventions (setting, type and intensity of exercise) and outcome measurements reported in the trials. Moreover, the risk of bias was high for all the identified studies.

None of the four included studies considered all-cause or cardiovascular mortality as outcomes.

None of the four included studies considered any cardiovascular event, such as acute myocardial infarction or stroke, as an outcome.

Two studies considered total cardiovascular risk as an outcome. There appeared to be no significant differences between the study groups ( Mendivil 2006 , Hellenius 1993 ). One of the studies reported an 8.38% (standard error (SE) 1.1) and 10.71% (SE 1.5) average probability of suffering a cardiovascular event at 10 years after the 16-week follow-up in the experimental and the control groups, respectively (P = 0.054) ( Mendivil 2006 ).

The other study, which did not describe the baseline values for the total cardiovascular risk, only reported the differences observed in each group: -0.87% (95% CI -1.57 to -0.17) in the exercise group versus 0.21% (95% CI -0.52 to 0.94) in the control group. Additionally, the average 10-year Framingham risk score by the end of the six months of intervention was estimated to be 8.8% (95% CI 5.5 to 12.5) in the exercise group and 9.1% (95% CI 6 to 13.5) in the control group. The differences between groups were not significant ( Hellenius 1993 ).

Clarifai’s Android SDK enables machine learning directly on your device, bypassing the traditional requirement of internet connectivity and extensive computing power. It makes it easy to use image and video recognition on device and in real-time.

The Android SDK is currently available in Private Beta. You can get more information about the program here.

Since the Android SDK is in a Private Beta program, you need to apply to get access. Once you are accepted into the program, you will receive email instructions on how to install the SDK and get setup.

In order to run the SDK, you would require a Clarifai account. Please Charles By Charles David Latrice Tropical Floral Wedge Sandal WcSBz
before proceeding with this guide.

Clarifai Android SDK supports applications running on Android API 21 (Version 5.0 “Lollipop”), or later.

The Clarifai SDK is initialized by calling Clarifai.start(applicationContext, apiKey); within the com.clarifai.clarifai_android_sdk.core.Clarifai package. We recommend starting it when your app has finished launching, but that is not absolutely required. Furthermore, work is offloaded to background threads, so there should be little to no impact on the launching of your app.

The SDK is built around a simple idea. You give inputs (images) to the library and it returns predictions (concepts). You need to add inputs to make predictions on it.,

All inputs are created from a DataAsset object in the Android SDK. A Data Asset is a container for the asset in question, plus metadata related to it. You can create a DataAsset initialized with an Image on Device or from a URL as shown in the example below.

Clarifai has a variety of Pre-Built Models to predict against. Android SDK currently supports General Model only to be loaded on device. As the product evolves, more Pre-Built models will be added to the SDK. The sample code below shows how to load the General Model and make a prediction against it.

Just as with our API, you can use the predict functionality on device with any of our available Pre-Built Models. Predictions generate outputs. An output has a similar structure to an input. It contains a data asset and concepts. The concepts associated with an output contain the predictions and their respective score (degree of confidence.)

The API is built around a simple idea. You send inputs (images) to the service and it returns predictions. In addition to receiving predictions on inputs, you can also 'save' inputs and their predictions to later search against. You can also 'save' inputs with concepts to later train your own model.

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