## 3 Results
### Detection of the Required Calibration Moment--Neural Networks  
The initial step is to identify weaknesses in the control processes, such as quality control parameters that do not accurately reflect the true variability of the measurements of the analyzers due to small initial study samples and the excessive precision of measurements on control materials. Further efforts should focus on identifying mechanisms to achieve quality control by determining appropriate methods for analyzing data from real patient measurements [21]. Quality control (QC) based on measurements of proprietary control materials covers only the analytical stage of the result generation process. Patient-based quality control techniques have been described for over fifty years and have been widely used in hematology for forty years [22]. However, due to practical issues, they are not widely applied in clinical chemistry laboratories. Nevertheless, recently, due to the availability of intermediary software and a greater appreciation of the benefits of these processes, there has been an interest in exploring their use as quality control tools. One method of such analysis is the assessment of \"averages of normals\" (AONs) proposed by [PERSON]. The purpose of implementing these assumptions was to create models for estimating the compatibility of statistical methods with the actual results obtained on analyzers during quality control studies and after device recalibration. One of the elements involves identifying the parameters of the AoN method [23]. * Exclusion from the set of measurement values that fall outside the reference ranges [24]. * Determining the number of results necessary for calculating averages (e.g., using the Cembrowski procedure). * Establishing control ranges. * Developing a model to assess the impact of recalibrating analyzers on the statistical characteristics of the results obtained on the devices [25]. MA QC, also known as Patient-Based Real-Time Quality Control (PBRTQC), is a mathematical procedure that averages patient test results in real time and uses the obtained mean values for quality control purposes [26]. Patient-based QC generally uses the mean, but other algorithms, including the median, exponentially weighted moving average, and others, have also been developed and evaluated. The effectiveness of methods based on patient results depends heavily on the selected cutoff levels. Reference ranges have been studied for decades, yet there is still no effective and universal method for determining cutoff points, as noted by [PERSON] [27]. It is important to use precise technical terms and avoid figurative language when discussing scientific concepts. The degree of interpersonal variability in a measured analyte, or the variance at the interindividual level, plays a significant role. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analyzer ID**} & **\% of Load** & \multirow{2}{*}{**Average Daily**} & **Standard** & **Average Daily** & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**\% Reduction in**} \\  & **Prediction** & & **Load** & **Deviation of** & **Load** & **Daily Load** & **Load Time** \\  & **Accuracy** & & **Daily Load** & **(Attransfer)** & **(Attransfer)** & **(Attransfer)** \\ \hline
1442906466 & 81.74\% & 48.04\% & 0.10 & 41.09\% & 0.08 & 14.22\% \\
1460164480 & 83.67\% & 46.33\%  
In laboratory practice, quality control (QC) tests are routinely conducted both daily in the morning and when there is a change in Lot or Batch, referring to the reagent used for analyses on the device. Recalibration is performed when QC checks reveal any discrepancies, as well as according to a predetermined schedule. The aim of the study in the reported task was to identify which quality controls, especially recalibrations, were unnecessary. The need for corrective action resulting from inaccurate quality control outcomes can be verified or refuted based on patient test results (see Figure 7).