# Enhancing Industrial Processes Through Augmented Reality: A Scoping Review
## 2 Methodology
### Paper Selection  
For the selection of articles, we used the PRISMA methodology flowchart, which allows us to represent the information through the different phases of a systematic review. This includes the number of records identified, the articles included and excluded, as well as the reasons for exclusions. This diagram was divided into three phases: identification, selection and inclusion. In the identification phase, 437 articles were found in the four databases and publishers; in the first step of this phase, 23 duplicate articles were found of which 414 articles were left for the selection phase. In this phase, as a first step, 306 articles were eliminated according to the reading of titles and abstracts; then, as a second step of the selection phase, the 108 articles were analysed to eliminate 25 according to the eligibility criteria indicated in Table 2. \begin{table}
\begin{tabular}{c c c} \hline
**Abbreviation** & **Criteria** & **Description** \\ \hline C1 & Study Design & Articles related to applications of AR in industrial processes. \\ C2 & Language & Only articles written in English were selected. \\ C3 & Time Range & Articles published between 2019 and 2024 were selected. \\ C4 & Significant Contribution & Articles that provide information on the applications of AR in engineering \\  & & through implementation and obtaining tangible results. \\ \hline \end{tabular}
\end{table} 
Table 2: Criteria and descriptions. In addition, as the third step of the selection phase, the remaining 83 articles were screened to eliminate 37 articles according to their relevance and perspective. Subsequently, 46 full articles were assessed for eligibility. Of these, three articles were excluded because they did not meet the appropriate study type for the purposes of the review. Then, using a 'Cochrane risk of bias' analysis, which refers to the possibility that the results of a study in a systematic review are distorted due to certain factors, we assessed the potential biases in the included studies. This rigorous selection process ensures the quality and reliability of the studies included in the systematic review. The seven bias domains were adapted to the review to five domains by the investigators [17]. These domains were:  
* D1. Inclusion criteria bias: Inaccuracy of selection criteria will lead to bias [18]: According to the research objective, this article clearly delimits the topic of study and the type of literature review approach, thus establishing the keywords to be searched for. Criteria, such as period and search language, are established to control any potential bias. In this domain, it was decided to include articles that despite not being within the established time period are of high relevance to the research. * D2. Data completeness bias: This refers to the completeness and accuracy of the data collected during the assessment of industrial processes and whether methods were applied to ensure the objectivity and validity of the results. * D3. Methodological quality bias: To assess the methodological quality of the studies included in the review, considering aspects such as study design, sample size, blinding and other elements that may influence the validity of the results, specifically in the context of AR in industrial processes. * D4. Evidence synthesis: Synthesising the results of the studies included in the review in a transparent and objective manner, considering the risk of bias in the interpretation of the results. This involves careful assessment of consistency and heterogeneity among studies, as well as consideration of possible bias in the interpretation of results. It allows the identification of all the components necessary to judge the risk of bias in reporting to improve the credibility of evidence syntheses. * D5. Researcher experience bias: Allows the identification of whether he or she or the authors consciously or unconsciously influence the results of the study, either in the design of the study or the interpretation of the data, which may affect the validity of the results.