Portfolio Research Assignment Sample

Introduction of Portfolio Research Assignment

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1. Research Philosophy

The research philosophy has been applied to the study to understand and evaluate the major experimental dimension of the study. In the study of Coskun and Oosterhuis (2020), has been applied the most viable research philosophy of pragmatism. Therefore, the study of Farrance et al. (2018), has implied the positivism philosophy of research. Thus, the chosen papers are commonly involved in applying significant tools of philosophy to create optimal structure and outcome for the study (Dougherty et al. 2019). Therefore, in the account of demonstrating selecting studies' philosophical application, Favresse et al. (2018), in the clinical account of investigating the most adequate incorporation study has followed positivism philosophy. Henceforth, the study of Milinkovi? et al. (2021) on uncertainty underpainting is being added in with major philosophical interception and causation profiling for future study outcomes. In the study of Church et al. (2020), the RNA analysis with technological and clinical approaches has followed the philosophical interventions of positivism. According to Žukauskas et al. (2018), have mentioned that the major philosophical tool used by clinical research as well as empirical research is positivism. The present article has also used the same tool for structuring.

2. Scientific misconduct

“Measurement uncertainty (MU)” becomes one of the major results that have been recommended as the principal statistical procedure in laboratory medicine (Milinkovi? et al. 2021). This is recognised as the analytical as well as the clinical analysis and evaluation of the known laboratory quality of the test accessed in clinical science. Scientific misconduct has mainly denoted the violation of the most reliable standard codes based on the ethical behavioural patterns and the conduct of the selected article in terms of specific “professional scientific research” procedures (Farrance et al. 2018). The selected article mainly denotes that certain MU issues have been highlighted in the article based on the data that have been interpreted in the medical laboratories for further research activities. There are several types of errors or misconduct that have been identified such as:

• “Misappropriation of the ideas”
• “Impropriety of Authorship”
• Failing to comply with both regulatory and legislative requirements
• Violation of the major accepted research activities and practices
• False data
• Fail to validate the research practices

The above-mentioned issues can be resolved with certain techniques and strategies like controlling certain effective variables that need to be more particular to the research article (Soltan et al. 2022). The techniques of measurement need to be improved by enhancing the randomization process for reducing the simple bias of the study.

The impact of scientific misconduct in the research field really provides a great negative effect that can “irreparably” erode the trust issues amongst all the colleagues. Certain trust issues also arise between the major funding agencies and the scientific researchers becomes a difficult situation for them to receive the most reliable grants for conducting further research activities (Church et al. 2020). The misinformation in the literature on the research field becomes very much harmful as the researcher can severely lose the federal type of funding and becomes more restricted to the supervised research activities by losing their jobs.

3. Effective literature searching

The main purpose of the clinical text based on MU needs to be determined as early as possible that become a “quantitative indicator” for the result of measurement of the clinical laboratory tests (Milinkovi? et al. 2021). This is recognised as the analytical as well as the clinical analysis and evaluation of the known laboratory quality of the test accessed in clinical science. According to Soltan et al. (2022), the rapid and fast laboratory free test and healthcare services are provided to Covid-19 patients during emergency cases. This surely helps to improve the validation of the research services for the betterment of the patients. The application of artificial intelligence (AI), mainly helps to deploy the entire services in order to make the services more flexible while screening the patients along with their laboratory screening techniques. On the other hand, AlSadah et al. (2019), state that the clinical laboratory values are reported critically to improve the communication skill among the entire workforce based on the research activities in the laboratory.

“Robust quality assurance system” is being adopted by the pathologists for providing the best quality of treatment and services by improving their communication techniques among the major identified stakeholders. Farrance et al. (2018) state that MU provides certain errors and defects that is very much harmful to the clinical laboratories in order to make the research activities more smoothly. The clinical data is being evaluated properly with the help of several measures techniques and tools for increasing the efficiency power of accessing the informative data in the laboratory. As per the views of Coskun and Oosterhuis (2020), statistical types of distributions are utilised in the MU in the laboratory clinic and the medicines to interpret the most reliable and authentic data for the correct evaluation. The statistical data and the standards are used for determining the total uncertainty in the clinical laboratory for assessing the data properly based on the collected resources.

According to Church et al. (2020), the gene cycle is being sequenced properly for identifying the bacterial implications in order to access the performance as well as the application of the technique performed in the clinical microbiological laboratory. The technology is being used for focusing on the application of the most recent and modern approaches that are used to determine the medical implications based on the bacterial pathogens of the clinical resources. On the other hand, Favresse et al. (2018), the analytical, pre-analytical as well as post-analytical variables are identified based on the execution of the clinical applications for diagnosing the intravascular type of coagulation in the clinical practices. This surely helps to improve the validation of the research services for the betterment of the patients. The pre-analytical variables mainly measure the D-dimer implications by discussing their performance level and assessing the clinical settings of the laboratory.

As per the views of Lippa et al. (2022), “MS?based untargeted metabolomics and lipidomics” are being used for reviewing the quality assurance of the clinical practices that can control the data analysis portion of the highly qualified data. “Standard operating procedures” are used for promoting, and describing the major implications that can surely improve the standard operations for enhancing the metabolomic of the entire clinical study. The clinical data is being evaluated properly with the help of several measures techniques and tools for increasing the efficiency power of accessing the informative data in the laboratory. This is recognised as the analytical as well as the clinical analysis and evaluation of the known laboratory quality of the test accessed in clinical science. According to Molavi et al. (2020), “Hypercholesterolemia” becomes one of the major diseases that have been identified which often lead to “atherosclerosis”. The lipid profile of the entire assessment is being performed properly for the betterment of the children and adolescents in order to diagnose the disorder as early as possible. The technology is being used for focusing on the application of the most recent and modern approaches that are used to determine the medical implications. The methods are analysed properly for the evaluation of the techniques in order to balance the lipid level of all the children for the betterment of the individuals.

4. Ethical Consideration

The study of Milinkovi? et al. (2021), has been involved with creating significant understanding and account of the uncertainty in case of laboratory practices. Therefore, the study has been associated with identifying the major acquisition and interpretation development of the extracted information. The extraction of information has been involved with consi9deration main ethics and principles of doing research. The measured entity is the core element of this research, hence, the performing adjustment and regulative ethical consideration have been followed authenticity of data. The paper has been collected data for identifying the uncertainty. According to Cascio et al. (2021), the ethical consideration for significant and natural research studies are mainly involved with the authenticity of data. However, the statistical data are the main risk worthy to developed threats of wrong input of data for a calculator which is required to adjust and mitigated with statistical tools.

The population-based data commonly lead to falsified data in addition. Thus, the false data reduction with repetitive regression calculation is significant. The ethical considerations of the study were mainly formed with quantitative data induction with impactful sources. According to Vlahou et al. (2021), under the data protection act, the generalized data are eventually lost. Therefore, the legal ethical interferences are applied within this study to maintain the research development with appropriate evaluation. The laboratory practices are commonly involved with numerical data examination and protection. MU used to be involved with arranging and proving major functionality. Therefore, the routine laboratory medicine practice account with this study and the outcomes with ethical consideration application ahs allowed maintaining the truthiness and significance. The interesting ethical consideration with negative impact developing for studies is threatening the overall measurements. In the common interest developing aspect of the other research with the same objective may be imposed different types of ethical considerations. Hence, the selected study is focused on data protection and truth data inputs from authentic sites to deliver major impact and outcomes highlighted.

References

Selected article

Milinkovi?, N., Jovi?i?, S. and Ignjatovi?, S., 2021. Measurement uncertainty as a universal concept: can it be universally applicable in routine laboratory practice?. Critical Reviews in Clinical Laboratory Sciences, 58(2), pp.101-112.

Articles for literature searching

AlSadah, K., El-Masry, O.S., Alzahrani, F., Alomar, A. and Ghany, M.A., 2019. Reporting Clinical Laboratory Critical Values: A Focus On The Recommendations Of The American College Of Pathologists. Journal of Ayub Medical College Abbottabad, 31(4), pp.612-618.

Church, D.L., Cerutti, L., Gürtler, A., Griener, T., Zelazny, A. and Emler, S., 2020. Performance and application of 16S rRNA gene cycle sequencing for routine identification of bacteria in the clinical microbiology laboratory. Clinical Microbiology Reviews, 33(4), pp.e00053-19.

Coskun, A. and Oosterhuis, W.P., 2020. Statistical distributions commonly used in measurement uncertainty in laboratory medicine. Biochemia Medica, 30(1), pp.5-17.

Farrance, I., Badrick, T. and Frenkel, R., 2018. Uncertainty in measurement and total error: different roads to the same quality destination?. Clinical Chemistry and Laboratory Medicine (CCLM), 56(12), pp.2010-2014.

Favresse, J., Lippi, G., Roy, P.M., Chatelain, B., Jacqmin, H., Ten Cate, H. and Mullier, F., 2018. D-dimer: Preanalytical, analytical, postanalytical variables, and clinical applications. Critical reviews in clinical laboratory sciences, 55(8), pp.548-577.

Lippa, K.A., Aristizabal-Henao, J.J., Beger, R.D., Bowden, J.A., Broeckling, C., Beecher, C., Clay Davis, W., Dunn, W.B., Flores, R., Goodacre, R. and Gouveia, G.J., 2022. Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC). Metabolomics, 18(4), pp.1-29.

Molavi, F., Namazi, N., Asadi, M., Sanjari, M., Motlagh, M.E., Shafiee, G., Qorbani, M., Heshmat, R. and Kelishadi, R., 2020. Comparison common equations for LDL-C calculation with direct assay and developing a novel formula in Iranian children and adolescents: the CASPIAN V study. Lipids in health and disease, 19(1), pp.1-8.

Soltan, A.A., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M.A., Thickett, D.R., Fairhead, R. and Zhu, T., 2022. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. The Lancet Digital Health, 4(4), pp.e266-e278.

Other articles

Cascio, M.A., Weiss, J.A. and Racine, E., 2021. Making autism research inclusive by attending to intersectionality: A review of the research ethics literature. Review Journal of Autism and Developmental Disorders, 8(1), pp.22-36.

Dougherty, M.R., Slevc, L.R. and Grand, J.A., 2019. Making research evaluation more transparent: Aligning research philosophy, institutional values, and reporting. Perspectives on Psychological Science, 14(3), pp.361-375.

Vlahou, A., Hallinan, D., Apweiler, R., Argiles, A., Beige, J., Benigni, A., Bischoff, R., Black, P.C., Boehm, F., Céraline, J. and Chrousos, G.P., 2021. Data sharing under the general data protection regulation: time to harmonize law and research ethics?. Hypertension, 77(4), pp.1029-1035.

Žukauskas, P., Vveinhardt, J. and Andriukaitien?, R., 2018. Philosophy and paradigm of scientific research. Management culture and corporate social responsibility, 121.