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1.  Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study 
By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education.
To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency).
A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the “Profile of Mood States” questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity.
Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a selectivity of RPB=0.2. For mARble, fatigue (z=2.214, P=.03) and numbness (z=2.07, P=.04) decreased with statistical significance when comparing pre- and post-tests. Vigor rose slightly, while irritability did not increase significantly. Changes in the control group were insignificant. Regarding hedonic quality (identification, stimulation, attractiveness), there were significant differences between mARble (mean 1.179, CI −0.440 to 0.440) and the book chapter (mean −0.982, CI −0.959 to 0.959); the pragmatic quality mean only differed slightly.
The mARble group performed considerably better regarding learning efficiency; there are hints for activating components of the mAR concept that may serve to fascinate the participants and possibly boost interest in the topic for the remainder of the class. While the small sample size reduces our study’s conclusiveness, its design seems appropriate for determining the effects of interactive eLearning material with respect to emotions, learning efficiency, and hedonic and pragmatic qualities using a larger group.
Trial Registration
German Clinical Trial Register (DRKS), DRKS-ID: DRKS00004685;
PMCID: PMC3758026  PMID: 23963306
problem-based learning; cellular phone; education; medical; emotions
2.  Diagnosis and treatment of cancer in medical textbooks of ancient Iran 
Research shows that ancient Iranians were among the pioneers of medical science, and are therefore admired and praised by non-Iranian scholars for their efforts and accomplishments in this field. Investigations of medical and historical texts indicate that between the 10th and the 18th century A.D., ancient Iran experienced a golden age of medicine. Great physicians such as Rhazes, al-Ahwazi, Avicenna and others reviewed the medical textbooks of civilizations such as Greece and India, Theories were scientifically criticized, superstitious beliefs were discarded, valuable innovations were added to pre-existing knowledge and the ultimate achievements were compiled as precious textbooks. Alhawi by Rhazes, Cannon by Avicenna, and Kamil al-Sina’ah by al-Ahwazi are among the works that were treasured by domestic and foreign scientists alike, as well as future generations who continued to appreciate them for centuries.
The above-mentioned textbooks discuss diseases and conditions related to neurosurgery, ophthalmology, ear, nose and throat, gastroenterology, urology, skeletomuscular system and other specialties, as well as cancer and similar subjects. One of the richest texts on the description, diagnosis, differential diagnosis, and prognosis of cancer and therapeutic approaches is Alhawi by Mohammad ibn Zakarya al Razi (Rhazes).
This article presents a brief summary of Rhazes’ views about the definition of cancer, types, signs and symptoms, prevalence, complications, medical care, treatment and even surgical indications and contraindications. Moreover, his opinions are compared against the views of other physicians and theories of modern medicine. It is also recommended to review the medical heritage of Iran and evaluate the proposed treatments based on modern methodologies and scientific approaches.
PMCID: PMC4263387  PMID: 25512835
cancer; Iranian ancient medicine; Rhazes
3.  Towards an Intelligent Textbook of Neurology 
We define an intelligent textbook of medicine to be a computer system that: (1) provides for storage and selective retrieval of synthesized clinical knowledge for reference purposes; and (2) supports the application by computer of its knowledge to patient information to assist physicians with decision making. This paper describes an experimental system called KMS (a Knowledge Management System) for creating and using intelligent medical textbooks. KMS is domain-independent, supports multiple inference methods and representation languages, and is designed for direct use by physicians during the knowledge acquisition process. It is presented here in the context of the development of an Intelligent Textbook of Neurology. We suggest that KMS has the potential to overcome some of the problems that have inhibited the use of knowledge-based systems by physicians in the past.
PMCID: PMC2203647
4.  Analysis of prescription database extracted from standard textbooks of traditional Dai medicine 
Traditional Dai Medicine (TDM) is one of the four major ethnomedicine of China. In 2007 a group of experts produced a set of seven Dai medical textbooks on this subject. The first two were selected as the main data source to analyse well recognized prescriptions.
To quantify patterns of prescriptions, common ingredients, indications and usages of TDM.
A relational database linking the prescriptions, ingredients, herb names, indications, and usages was set up. Frequency of pattern of combination and common ingredients were tabulated.
A total of 200 prescriptions and 402 herbs were compiled. Prescriptions based on "wind" disorders, a detoxification theory that most commonly deals with symptoms of digestive system diseases, accounted for over one third of all prescriptions. The major methods of preparations mostly used roots and whole herbs.
The information extracted from the relational database may be useful for understanding symptomatic treatments. Antidote and detoxification theory deserves further research.
PMCID: PMC3485134  PMID: 22931752
Traditional Dai medicine; Dai medical textbooks; Dai prescription
5.  How Current Are Leading Evidence-Based Medical Textbooks? An Analytic Survey of Four Online Textbooks 
The consistency of treatment recommendations of evidence-based medical textbooks with more recently published evidence has not been investigated to date. Inconsistencies could affect the quality of medical care.
To determine the frequency with which topics in leading online evidence-based medical textbooks report treatment recommendations consistent with more recently published research evidence.
Summarized treatment recommendations in 200 clinical topics (ie, disease states) covered in four evidence-based textbooks–UpToDate, Physicians’ Information Education Resource (PIER), DynaMed, and Best Practice–were compared with articles identified in an evidence rating service (McMaster Premium Literature Service, PLUS) since the date of the most recent topic updates in each textbook. Textbook treatment recommendations were compared with article results to determine if the articles provided different, new conclusions. From these findings, the proportion of topics which potentially require updating in each textbook was calculated.
478 clinical topics were assessed for inclusion to find 200 topics that were addressed by all four textbooks. The proportion of topics for which there was 1 or more recently published articles found in PLUS with evidence that differed from the textbooks’ treatment recommendations was 23% (95% CI 17-29%) for DynaMed, 52% (95% CI 45-59%) for UpToDate, 55% (95% CI 48-61%) for PIER, and 60% (95% CI 53-66%) for Best Practice (χ 2 3=65.3, P<.001). The time since the last update for each textbook averaged from 170 days (range 131-209) for DynaMed, to 488 days (range 423-554) for PIER (P<.001 across all textbooks).
In online evidence-based textbooks, the proportion of topics with potentially outdated treatment recommendations varies substantially.
PMCID: PMC3799557  PMID: 23220465
databases, bibliographic; medical informatics; evidence-based medicine
8.  A Long-Needed Textbook 
CBE Life Sciences Education  2010;9(1):22-24.
PMCID: PMC2830158
13.  Knowledge requirements for automated inference of medical textbook markup. 
Indexing medical text in journals or textbooks requires a tremendous amount of resources. We tested two algorithms for automatically indexing nouns, noun-modifiers, and noun phrases, and inferring selected binary relations between UMLS concepts in a textbook of infectious disease. Sixty-six percent of nouns and noun-modifiers and 81% of noun phrases were correctly matched to UMLS concepts. Semantic relations were identified with 100% specificity and 94% sensitivity. For some medical sub-domains, these algorithms could permit expeditious generation of more complex indexing.
PMCID: PMC2232726  PMID: 10566445
14.  Automated Text Markup for Information Retrieval from an Electronic Textbook of Infectious Disease 
The information needs of practicing clinicians frequently require textbook or journal searches. Making these sources available in electronic form improves the speed of these searches, but precision (i.e., the fraction of relevant to total documents retrieved) remains low. Improving the traditional keyword search by transforming search terms into canonical concepts does not improve search precision greatly.
Kim et al. have designed and built a prototype system (MYCIN II) for computer-based information retrieval from a forthcoming electronic textbook of infectious disease. The system requires manual indexing by experts in the form of complex text markup. However, this mark-up process is time consuming (about 3 person-hours to generate, review, and transcribe the index for each of 218 chapters).
We have designed and implemented a system to semiautomate the markup process. The system, information extraction for semiautomated indexing of documents (ISAID), uses query models and existing information-extraction tools to provide support for any user, including the author of the source material, to mark up tertiary information sources quickly and accurately.
PMCID: PMC2232314
15.  Medical Textbook Summarization and Guided Navigation using Statistical Sentence Extraction 
We present a method for automated medical textbook and encyclopedia summarization. Using statistical sentence extraction and semantic relationships, we extract sentences from text returned as part of an existing textbook search (similar to a book index). Our system guides users to the information they desire by summarizing the content of each relevant chapter or section returned through the search. The summary is tailored to contain sentences that specifically address the user’s search terms. Our clustering method selects sentences that contain concepts specifically addressing the context of the query term in each of the returned sections. Our method examines conceptual relationships from the UMLS and selects clusters of concepts using Expectation Maximization (EM). Sentences associated with the concept clusters are shown to the user. We evaluated whether our extracted summary provides a suitable answer to the user’s question.
PMCID: PMC1560740  PMID: 16779153
20.  Stewart's Textbook of Acid-Base, 2nd edition 
Critical Care  2009;13(3):306.
PMCID: PMC2717453
21.  Pediatric Gastrointestinal Endoscopy Textbook and Atlas 
Gut  2007;56(8):1176.
PMCID: PMC1955500

Results 1-25 (37312)