Methods: Arksey and O'Malley's (2005) approach to scoping reviews was used to identify appropriate publications featured in four databases published between 1 January 1990 and 31 December 2018. Seven members of the research team employed thematic analysis to evaluate the selected articles.
Results: 3799 abstracts were identified, 138 full-text articles reviewed and 74 studies included. The two themes identified were the context-specific nature of assessments and competency-based stages in medical professionalism.
Conclusions: Prevailing assessments of professionalism in medicine must contend with differences in setting, context and levels of professional development as these explicate variances found in existing assessment criteria and approaches. However, acknowledging the significance of context-specific competency-based stages in medical professionalism will allow the forwarding of guiding principles to aid the design of a culturally-sensitive and practical approach to assessing professionalism.
DESIGN: 1805 consecutive unselected patients with FGID who presented for primary or secondary care to 11 centres across Asia completed a cultural and linguistic adaptation of the Rome III Diagnostic Questionnaire that was translated to the local languages. Principal components factor analysis with varimax rotation was used to identify symptom clusters.
RESULTS: Nine symptom clusters were identified, consisting of two oesophageal factors (F6: globus, odynophagia and dysphagia; F9: chest pain and heartburn), two gastroduodenal factors (F5: bloating, fullness, belching and flatulence; F8 regurgitation, nausea and vomiting), three bowel factors (F2: abdominal pain and diarrhoea; F3: meal-related bowel symptoms; F7: upper abdominal pain and constipation) and two anorectal factors (F1: anorectal pain and constipation; F4: diarrhoea, urgency and incontinence).
CONCLUSION: We found that the broad categorisation used both in clinical practice and in the Rome system, that is, broad anatomical divisions, and certain diagnoses with long historical records, that is, IBS with diarrhoea, and chronic constipation, are still valid in our Asian societies. In addition, we found a bowel symptom cluster with meal trigger and a gas cluster that suggests a different emphasis in our populations. Future studies to compare a non-Asian cohort and to match to putative pathophysiology will help to verify our findings.
METHOD: The primary data source of the study consists of excerpts of verbal expressions within the familial context. The provenance of the locational data can be traced to a familial unit with a cultural legacy deeply embedded in Javanese customs. The data were collected using observation and participation methodologies, employing advanced techniques of recording and note taking. The data were categorized and characterized to identify the various data types and formats. The tabulated results of classification and typification are presented to triangulate theory through expert validation and justification of theories. The method of contextual analysis was utilized to conduct the data analysis that relies on the pragmatic context.
RESULTS: The study's findings indicate the following: The Javanese language encompasses various modes of pseudo-directive utterances, such as commanding, ordering, suggesting, insinuating, and recommending. In addition, the Javanese language encompasses pseudo-directive pragmatics such as warning, prohibiting, reminding, suggesting, and commanding.
CONCLUSION: This research will significantly assist in formulating a pragmatic framework that considers cultural factors, as other linguistic phenomena in various regional languages remain unresolved.
RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).
CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.