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Randy Goodall
Randy Goodall

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Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.




1gb Sample Pdf File 230


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The concepts explained in this section are illustrated in Additional file 1, with the validation of the Risk of Ovarian Malignancy Algorithm (ROMA) for the diagnosis of ovarian malignancy in women with an ovarian tumor selected for surgical removal [28]; further details can be found elsewhere [1, 4, 29].


Third, moderate calibration implies that estimated risks correspond to observed proportions, e.g., among patients with an estimated risk of 10%, 10 in 100 have or develop the event. This is assessed with a flexible calibration curve to show the relation between the estimated risk (on the x-axis) and the observed proportion of events (y-axis), for example, using loess or spline functions. A curve close to the diagonal indicates that predicted risks correspond well to observed proportions. We show a few theoretical curves in Fig. 1a,b, each of which corresponds to different calibration intercepts and slopes. Note that a calibration intercept close to 0 and a calibration slope close to 1 do not guarantee that the flexible calibration curve is close to the diagonal (see Additional file 1 for an example). To obtain a precise calibration curve, a sufficiently large sample size is required; a minimum of 200 patients with and 200 patients without the event has been suggested [4], although further research is needed to investigate how factors such as disease prevalence or incidence affect the required sample size [12]. In small datasets, it is defendable to evaluate only weak calibration by calculating the calibration intercept and slope.


When developing a predictive algorithm, the first step involves the control of statistical overfitting. It is important to prespecify the modeling strategy and to ensure that sample size is sufficient for the number of considered predictors [30, 31]. In smaller datasets, procedures that aim to prevent overfitting should be considered, e.g., using penalized regression techniques such as Ridge or Lasso regression [32] or using simpler models. Simpler models can refer to fewer predictors, omitting nonlinear or interaction terms, or using a less flexible algorithm (e.g., logistic regression instead of random forests or a priori limiting the number of hidden neurons in a neural network). However, using models that are too simple can backfire (Additional file 1), and penalization does not offer a miracle solution for uncertainty in small datasets [33]. Therefore, in small datasets, it is reasonable for a model not to be developed at all. Additionally, internal validation procedures can quantify the calibration slope. At internal validation, calibration-in-the-large is irrelevant since the average of predicted risks will match the event rate. In contrast, calibration-in-the-large is highly relevant at external validation, where we often note a mismatch between the predicted and observed risks.


When we find poorly calibrated predictions at validation, algorithm updating should be considered to provide more accurate predictions for new patients from the validation setting [1, 20]. Updating of regression-based algorithms may start with changing the intercept to correct calibration-in-the-large [34]. Full refitting of the algorithm, as in the case study below, will improve calibration if the validation sample is relatively large [35]. We present a detailed illustration of updating of the ROMA model in Additional file 1. Continuous updating strategies are also gaining in popularity; such strategies dynamically address shifts in the target population over time [36].


Rotor mills are used for high-speed size reduction of soft to medium-hard as well as temperature-sensitive or fibrous materials. The powerful Ultra Centrifugal Mill ZM 300 provides maximum grinding performance combined with ease of use. The variable speed from 6,000 to 23,000 rpm allows for gentle, neutral-to-analysis sample preparation in a very short time. Thanks to an integrated temperature monitoring system, reproducibility is guaranteed even for long grinding processes or pulverization of large sample volumes. The wide selection of rotors, ring sieves and cassettes makes the ZM 300 a true allrounder which meets the requirements of a great variety of size reduction tasks.


The maximum speed of classic centrifugal mills, like the widely used ZM 200, is usually limited to 18,000 rpm (rotor peripheral speed 98 m/s). The ZM 300 achieves a maximum speed of 23,000 rpm (rotor peripheral speed 118 m/s) and produces particles which are 15 to 20 % finer in comparison, depending on the material. The higher speed has a particularly positive impact on the grind sizes of polymer samples which are pulverized cryogenically, or of fibrous materials like hay. Compared to models with a maximum speed of 18,000 rpm, the throughput may be increased by 10 to 15 %.


Grind sizes of plastic materials (POM or PP) after grinding with different sieves and speeds. The speed of 23,000 rpm results in a higher fineness for all sieves compared to grinding at 18,000 rpm. For example, a 19 % reduction in fineness can be achieved when POM is ground with a 0.12 mm distance sieve at 23,000 rpm. The maximum sample throughput, e.g. when crushing chicken feed with a 0.5 mm ring sieve, could be increased by 16% when crushing at maximum speed of 23,000 min-1 instead of 18,000 min-1.


The fines content in a pulverized sample can be controlled by speed reduction. If, for example, the grains of an animal feed sample are to be coarsely ground to avoid dust formation, a reduction to 6,000 - 10,000 rpm will provide the desired result. Thanks to the variable speed, the ZM 300 can be flexibly adapted to all requirements in the food and feed industry, chemical industry, and in agriculture.


Cryogenic or cold grinding is the ideal solution for pulverizing samples that cannot be reduced to the required fineness at room temperature. This procedure involves the use of grinding aids such as liquid nitrogen (-196 C, embrittlement of the sample outside the mill) or dry ice (-78 C, sample/dry ice mixture) to embrittle the sample material by cooling, and thus improve the breaking behavior.In addition, highly volatile components are better preserved in the sample by cooling. Cryogenic grinding is easy to perform with the ZM 300 and is recommended especially for plastics or very temperature-sensitive samples. The video shows the process with the predecessor model ZM 200, which is identical in the ZM 300.


The wide selection of accessories allows optimum adaptation to all size reduction requirements. The desired fineness is determined by the aperture size of the ring sieve. As a rule of thumb, about 80 % of the sample is about half the size of the aperture size of the selected sieve after grinding. Sieves are available with aperture sizes ranging from 0.08 to 10 mm.


Rotors are available with either 6, 12 or 24 teeth. The standard rotor with 12 teeth is suitable for almost any material and requirement. For fibrous samples, such as straw, the rotor with 6 teeth is typically used, while for fine samples the rotor with 24 teeth is best suited.


In the standard collecting vessel with 900 ml nominal volume, up to 300 ml of sample can be ground in one working step. With the large-volume cassette, the useful volume can be doubled to 600 ml. When utilizing a cassette with cyclone, various collecting vessels up to 4,500 ml useful volume are available.


The Vibratory Feeder DR 100 is controlled via an interface and conveys material in a load-dependent manner to the hopper of the ZM 300. This procedure ensures uniform grinding with maximum sample feed. The use of a feeder is particularly advantageous for large sample quantities.


The ZM 300 is easy and safe to operate. The large touch display with rotary knob permits convenient entry of the grinding parameters. It shows the current cassette temperature and load during grinding which helps to prevent overloads by feeding the sample too quickly. A push-fit system without screws and the patented cassette principle allow for easy insertion and removal without tools. As a result, cleaning rotors and ring sieves is particularly quick and easy. All parts in contact with the sample can be cleaned under running water or in the dishwasher.


In the Ultra Centrifugal Mill ZM 300 size reduction takes place by impact and shearing effects between the rotor and the fixed ring sieve. The feed material passes through the hopper (with splash-back protection) onto the rotor. Centrifugal acceleration throws it outward with great energy and it is pre-crushed on impact with the wedge-shaped rotor teeth moving at a high speed. It is then finely ground between the rotor and the ring sieve. This 2-step grinding ensures particularly gentle but fast processing. The feed material only remains in the grinding chamber for a very short time, which means that the characteristic features of the sample to be determined are not altered. The ground sample is collected in the collecting cassette surrounding the grinding chamber or in the downstream cyclone or paper filter bag.


You may also consider using synchronization filters for Cached Mode profiles to reduce your .ost file size. The following blog post describes how to reduce the size of your local data file by using synchronization filters.


For Outlook 2013 and later versions, you can also try to use the Sync Slider feature for Cached Mode profiles. This feature lets you control how many months of email messages are synchronized with your .ost file. For more information about the Sync Slider feature, see Only a subset of your Exchange mailbox items are synchronized in Outlook. 350c69d7ab


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