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At least 73 records · Page 4Linked to original sources

Innominate osteotomy in the treatment of Legg-Calvé-Perthes disease.

The absolute treatment of extensively involved cases of Legg-Calvé-Perthes disease (LCPD) remains unsolved. The basic principle of adequate containment of the capital femoral epiphysis in its acetabulum while minimizing the disability time is widely accepted, but the definition of adequate containment and methods of achieving this goal are controversial. Some explanations for the ambiguity are the different criteria for patient selection and the greatly dissimilar methods of evaluating results. The innominate osteotomy can be an efficacious modality of treatment for those children with a femoral head "at risk," with a good range of hip motion, and if not in late Stage III or Stage IV disease. In addition, it may be indicated when conservative treatment in severe cases is not accepted or with poor compliance and in older children with subluxated femoral heads. However, each child should be evaluated and treated individually. This procedure currently does not complicate further reconstructive surgery, such as femoral osteotomies or total hip replacement. The patient and parents should have all of the aspects of the disease, especially its stages and groups, as well as the entire spectrum of treatment modalities and their ramifications, explained to them. Only then can an intelligent decision be made jointly by the physician, patient, and parents concerning management of LCPD.

Child↗

New ophthalmic lasers for the evaluation and treatment of retinal disease.

Ophthalmic lasers are used as diagnostic as well as therapeutic modalities in patients with retinal diseases. Recent advances in laser technology have allowed more convenient selection of wavelength and a wide variety of delivery systems is available to deliver the laser energy to the retina. In addition, newer lasers that disrupt and cut tissue will become available for use in patients with retinal diseases. The use of imaging lasers and lasers in combination with dyes selectively to enhance their effects have become useful in diagnosis and understanding of pathophysiology of diseases. These lasers will also be therapeutically useful. This manuscript delineates the new types of diagnostic and therapeutic lasers, new imaging dyes and techniques as they apply to retinal diseases as well as the principles of laser-tissue interaction as applied to the retina. This should allow the ophthalmologist who treats patients with retinal diseases to make more intelligent decisions regarding the use and acquisition of laser systems.

Animals↗

Informed consent or refusal.

Practicing dentists need a good understanding of consent issues. Dentists are required to disclose all material facts to a patient. A properly informed patient can then make intelligent decision regarding his or her care. This article describes the legal requirements of informed consent, both express and implied, and the exceptions to it. The article also discusses informed refusal and its effect care.

California↗

Decision-support and intelligent tutoring systems in medical education.

One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.

Artificial Intelligence↗

Artificial intelligence and Bayesian decision theory in the prediction of chemical carcinogens.

Two procedures for predicting the carcinogenicity of chemicals are described. One of these (CASE) is a self-learning artificial intelligence system that automatically recognizes activating and/or deactivating structural subunits of candidate chemicals and uses this to determine the probability that the test chemical is or is not a carcinogen. If the chemical is predicted to be carcinogen, CASE also projects its probable potency. The second procedure (CPBS) uses Bayesian decision theory to predict the potential carcinogenicity of chemicals based upon the results of batteries of short-term assays. CPBS is useful even if the test results are mixed (i.e. both positive and negative responses are obtained in different genotoxic assays). CPBS can also be used to identify highly predictive as well as cost-effective batteries of assays. For illustrative purposes the ability of CASE and CPBS to predict the carcinogenicity of a carcinogenic and a non-carcinogenic polycyclic aromatic hydrocarbon is shown. The potential for using the two methods in tandem to increase reliability and decrease cost is presented.

Animals↗

Effects of auditory fatigue on speech intelligibility and lexical decision in noise.

The influence of weak auditory fatigue on speech identification and lexical decision (word/nonword) was studied. It appears that, at a low listening level and in presence of a strong masking noise, auditory fatigue gives rise to: (1) a decrease in identification scores, (2) more frequent confusions for fricatives, (3) a reduction in correct lexical decision scores, (4) an increase in the tendency to respond "word" more often than "nonword" (5) a slight increase in reaction times for incorrectly repeated items. The intelligibility impairment can be attributed to a combination of masking and fatigue effects. Errors in lexical decisions and changes in response times are explained by an adaptation of the lexical decision processes to the degraded perceptual representations of the stimuli. There is no clear evidence in this experiment that a very central component of auditory fatigue influences lexical decisions.

Adult↗

Artificial intelligence: a computerized decision aid for trauma.

A computerized decision support system has been developed to advise ATLS-trained surgeons on the initial definitive management of patients with penetrating injuries of the abdomen immediately following resuscitation and stabilization. The program was developed as an "expert system," using the techniques of artificial intelligence. It is able to suggest: the need for further examination; additional tests; diagnoses; and treatments. In this study, the advice offered by the expert system was compared to that of physicians-in-training. Five actual patient care situations were presented to the system and to 13 medical students and surgical residents: four MS-III, three PGY-I, three PGY-III, and three PGY-V. The suggestions of each of the 13 trainees, the advice of the expert system, and the actual management were blinded. Five surgeons versed in trauma and otherwise not involved in the project judged whether each of the 15 purported management plans was acceptable and ranked them in order of preference. Only the actual care and the advice from the system were judged acceptable for all five problems. The rankings of the expert system were better than those of any individual trainee. The differences were statistically significant for two of the three chief residents, five of nine residents overall, and all four students. This preliminary validation of a prototype expert system is encouraging for the prospect of a computerized decision support system that can help surgeons make initial definitive management plans for patients with major trauma.

Abdominal Injuries↗

Development and assessment of an intelligent shelf life decision system for quality optimization of the food chill chain.

The principles of application of a Shelf Life Decision System (SLDS) for the optimization of the distribution of chilled fresh and minimally processed food products are developed. The SLDS integrates predictive kinetic models of food spoilage, data on initial quality from rapid techniques, and the capacity to continuously monitor temperature history of the food product with Time Temperature Integrators (TTIs) into an effective chill chain management tool that leads to an improved narrow distribution of quality at consumption time, effectively reducing the probability of products consumed past shelf life end. The applicability and effectiveness of the SLDS is demonstrated and evaluated based on actual food spoilage and TTI kinetics and chill chain data employing the Monte Carlo simulation method.

Animals↗

The Spot-the-Word test: a robust estimate of verbal intelligence based on lexical decision.

The development of a test aimed at estimating premorbid intelligence is described. The test, Spot-the-Word, involves presenting the subject with pairs of items comprising one word and one non-word, and requiring the subject to identify the word. Data show that performance correlates highly with verbal intelligence as estimated by Mill Hill Vocabulary score and by performance on the National Adult Reading Test (NART). Performance does not decline with age, in contrast to an associated test of verbal recognition memory. A second study attempted to test the effect of intellectual deterioration due to age on Spot-the-Word performance. Elderly subjects who had high vocabulary scores scored well on the Spot-the-Word regardless of whether fluid intelligence as measured by the AH4 test was well preserved, or was low, implying intellectual deterioration. A final study collected normative data on a sample of 224 subjects stratified by age and socio-economic status, with each subject performing two parallel forms of the test, A and B, together with the NART. Correlation between the two forms was .884, while correlation with NART was .831 for Form A and .859 for Form B, suggesting adequate reliability and validity. It is concluded that the test provides a potentially useful additional method of estimating premorbid intelligence.

Adolescent↗

Using statistical decision theory to predict speech intelligibility. I. Model structure.

This article introduces a new model that predicts speech intelligibility based on statistical decision theory. This model, which we call the speech recognition sensitivity (SRS) model, aims to predict speech-recognition performance from the long-term average speech spectrum, the masking excitation in the listener's ear, the linguistic entropy of the speech material, and the number of response alternatives available to the listener. A major difference between the SRS model and other models with similar aims, such as the articulation index, is this model's ability to account for synergetic and redundant interactions among spectral bands of speech. In the SRS model, linguistic entropy affects intelligibility by modifying the listener's identification sensitivity to the speech. The effect of the number of response alternatives on the test score is a direct consequence of the model structure. The SRS model also appears to predict the differential effect of linguistic entropy on filter condition and the interaction between linguistic entropy, signal-to-noise ratio, and language proficiency.

Decision Making↗

Intelligent knowledge retrieval for decision support in medical applications.

Knowledge management and retrieval are key issues to be addressed in the medical domain, where a large amount of information is generally available, and where the expert's skills need to be properly shared across the organisation, with the aim of improving the quality of care. Case Based Reasoning (CBR) is a very well suited methodology for the Knowledge Management task, when knowledge is in the operative form. Nevertheless, also well-assessed, formalised medical knowledge, such as clinical guidelines, should be made available to physicians in order to optimise their reasoning process. To take advantage of both knowledge types, we have defined a Multi Modal Reasoning methodology, that integrates CBR and Rule Based Reasoning, for supporting context detection, information retrieval and therapy revision in diabetes care.

Artificial Intelligence↗

The graphical presentation of decision support information in an intelligent anaesthesia monitor.

This contribution examines the graphical presentation of decision support information generated by an intelligent monitor, named SENTINEL, developed for use during anaesthesia. Clinicians make diagnoses in real-time during operations by examining clinically significant trends in multiple signals. SENTINEL attempts to mimic this decision process by using a system of fuzzy trend templates. SENTINEL's implementation of fuzzy trend templates is capable of providing the dual fuzzy measures of belief and plausibility, which are derived from the theory of evidence. It is thus capable of generating fairly rich diagnostic decision support information. However, for SENTINEL to be effective, the visual presentation of this information must be intuitive to the anaesthetist, who may not be familiar with the theory of evidence. This paper discusses techniques that are being evaluated to meet the requirements of the SENTINEL anaesthesia monitor. Specifically, the paper presents methods for highlighting clinically significant trends in physiological (or derived) signals by superimposing a coloured band on the signal that reflects fuzzy output from the intelligent monitor. This paper also discusses the intuitive graphical presentation of binary diagnostic fuzzy measures, including their further interpretation and presentation as crisp "alarm" and "warning" conditions.

Anesthesia, General↗

A therapy planning architecture that combines decision theory and artificial intelligence techniques.

Through our experience with the ONCOCIN cancer therapy consultation system, we have identified a set of medical planning problems to which no single existing computer-based reasoning technique readily applies. In response to the need for automated assistance with this class of problems, we have devised a computer program called ONYX that combines decision-theoretic and artificial intelligence approaches to planning. We discuss our rationale for devising a new planning architecture and describe in detail how that architecture is implemented. The program's planning process consists of three steps: (i) the use of rules derived from therapy planning strategies to generate a small set of plausible plans, (ii) the use of knowledge about the structure and behavior of the human body to create simulations that predict possible consequences of each plan for the patient, and (iii) the use of decision theory to rank the plans according to how well the results of each simulation meet the treatment goals. This architecture explicitly manages the uncertainty inherent in many planning tasks, introduces a possible mechanism for the dissemination of decision-theoretic therapy advice, and potentially increases the number of problem solving domains in which expert system techniques can be effectively applied.

Artificial Intelligence↗

Dynamics of sequential decision making.

We suggest a new paradigm for intelligent decision-making suitable for dynamical sequential activity of animals or artificial autonomous devices that depends on the characteristics of the internal and external world. To do it we introduce a new class of dynamical models that are described by ordinary differential equations with a finite number of possibilities at the decision points, and also include rules solving this uncertainty. Our approach is based on the competition between possible cognitive states using their stable transient dynamics. The model controls the order of choosing successive steps of a sequential activity according to the environment and decision-making criteria. Two strategies (high-risk and risk-aversion conditions) that move the system out of an erratic environment are analyzed.

Animals↗