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Computer-assisted learning in the teaching of anaesthesia.

A comparison of computer-assisted learning and small tutorial group teaching was carried out in the instruction of final year medical students in anaesthesia. The scores attained by fifteen of the sixteen students were higher after computer-assisted learning than after small group teaching. Statistical analyses of the results demonstrated that in this study computer-assisted learning was significantly better than small tutorial teaching.

Anesthesiology↗

Attitudes of medical undergraduates in Glasgow to computer-assisted learning.

Computer-assisted learning (CAL) has been introduced as part of the undergraduate teaching course in general practice during the penultimate year of the medical course. The student is given an opportunity to make clinical decisions and to manage a case over a significant time scale. The attitudes of the students are favourable to this method of instruction.

Attitude of Health Personnel↗

Quality assessment and the art of medicine: the anatomy of laceration care.

Assuring high quality medical care has remained an elusive goal because of several problems which have hampered development of effective medical audit programs: inadequate patient data, unreasonable evaluative criteria and insensitive audit procedures. The present study demonstrates the use of a clinical algorithm to help overcome these problems. An examination of medical record data from a series of 703 laceration patients treated in an emergency service yielded only 27 cases (4 per cent) with medical records sufficiently complete to use for auditing physician compliance with algorithmic criteria. Substituting a structured checklist for the handwritten note increased this rate to 86 per cent. A computer-assisted branching audit of 1,400 laceration cases demonstrated that 1) physician compliance with an algorithmic instruction varied significantly (p less than .001) according to the specific instruction, and 2) compliance with a given instruction varied significantly (p less than .001) across different providers. These results underscore the need for medical audit with educational feedback which is provider specific.

Connecticut↗

Fentanyl does not alter the "sleep" plasma concentration of thiopental.

Thiopental and fentanyl are commonly combined for induction of anesthesia. The effect of an analgesic concentration of fentanyl on the plasma concentration of thiopental to induce sleep was studied in 46 unpremedicated patients. As a measure of drug effect, sleep (the lack of response to open eyes to a verbal command) was used. Forty-six patients were randomized to receive thiopental infused to one of several predetermined plasma concentrations. Twenty-two of these patients also received a fentanyl infusion to a desired analgesic concentration of 1 ng/mL. Thiopental and fentanyl were infused by means of a pharmacokinetic model-driven infusion device (computer-assisted continuous infusion, CACI). Venous blood samples were taken from the contralateral antecubital fossa at 5 and 10 min after the start of the infusion. At 10 min, the patients' names were firmly spoken, and they were instructed to open their eyes. If they did not respond to this command, they were considered to be asleep. Only patients in whom the 5- and 10-min measured plasma concentrations of thiopental and fentanyl, respectively, were within +/- 30% of each other were used for the determination of the Cp50(asleep), the plasma concentration at which 50% of the patients were asleep. The Cp50(asleep) with and without fentanyl was calculated by logistic regression. The Cp50(asleep) for patients in whom concentrations were maintained within +/- 30% for thiopental alone (n = 17) was 7.32 micrograms/mL (95% confidence interval, 5.53-10.95); for thiopental in the presence of fentanyl (n = 18 with a measured fentanyl concentration of 1.27 +/- 0.5 ng/mL), this was 7.22 micrograms/mL (95% confidence interval, 4.83-10.15).(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

ROENTGEN: case-based reasoning and radiation therapy planning.

ROENTGEN is a design assistant for radiation therapy planning which uses case-based reasoning, an artificial intelligence technique. It learns both from specific problem-solving experiences and from direct instruction from the user. The first sort of learning is the normal case-based method of storing problem solutions so that they can be reused. The second sort is necessary because ROENTGEN does not, initially, have an internal model of the physics of its problem domain. This dependence on explicit user instruction brings to the forefront representational questions regarding indexing, failure definition, failure explanation and repair. This paper presents the techniques used by ROENTGEN in its knowledge acquisition and design activities.

Artificial Intelligence↗

Reducing heart rate reactivity to stress with feedback. Generalization across task and time.

Heart rate (HR) reactions to two behavioral stressors (videogame and mental arithmetic) were measured in 8 experimental subjects who received biofeedback training and 8 matched control subjects during three assessment periods: pretraining, posttraining, and one-week follow-up. Experimental subjects exhibited significant reductions in HR following a training session in which they received ongoing HR feedback while playing a videogame. Control subjects, who played the same number of videogames without HR feedback, showed smaller HR reductions. During the training session, all subjects were instructed to reduce HR while maximizing game performance. In comparison to controls, experimental subjects (a) maintained lower HRs during videogame presentations after a one-week period and (b) generalized these HR reductions to the mental arithmetic challenge at follow-up. Performance on the videogame declined from posttraining to follow-up for experimental subjects but not for control subjects. No group difference in mental arithmetic performance was observed.

Adolescent↗