4 research outputs found
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Ultrasound curriculum taught by first-year medical students: A four-year experience in Tanzania.
BACKGROUND:Diagnostic imaging is an integral aspect of care that is often insufficient, if not altogether absent, in rural and remote regions of low to middle income countries (LMICs) such as Tanzania. The introduction of ultrasound can significantly impact treatment in these countries due to its portability, low cost, safety, and usefulness in various medical assessments. This study reviews the implementation of a four-week ultrasound course administered annually from 2013-2016 in a healthcare professional school in Mwanza, Tanzania by first-year allopathic US medical students. METHODS:Participants (n=582, over 4 years) were recruited from the Tandabui Institute of Health Sciences and Technology to take the ultrasound course. Subjects were predominantly clinical officer students, but other participants included other healthcare professional students, practicing healthcare professionals, and school employees. Data collected includes pre-course examination scores, post-course examination scores, course quiz scores, demographic surveys, and post-course feedback surveys. Data was analyzed using two-tailed t-tests and the single factor analysis of variance (ANOVA). RESULTS:For all participants who completed both the pre- and post-course examinations (n=229, 39.1% of the total recruited), there was a significant mean improvement in their ultrasound knowledge of 42.5%, P<0.01. CONCLUSION:Our data suggests that trained first-year medical students can effectively teach a point of care ultrasound course to healthcare professional students within four weeks in Tanzania. Future investigation into the level of long-term knowledge retention, impact of ultrasound training on knowledge of human anatomy and diagnostic capabilities, and how expansion of an ultrasound curriculum has impacted access to care in rural Tanzania is warranted
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Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between non-epileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<0.01). Using VC, the average accuracy was significantly lower (39.2%). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data
Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between non-epileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<0.01). Using VC, the average accuracy was significantly lower (39.2%). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data