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2.
J Clin Anesth ; 15(5): 334-8, 2003 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-14507557

RESUMO

STUDY OBJECTIVE: To determine whether tracheoscopy is an accurate and quick method for verifying correct placement of the tracheal tube after intubation. DESIGN: Prospective, randomized study. SETTING: Operating rooms of a teaching hospital. PATIENTS: 26 patients scheduled for surgery and general anesthesia. INTERVENTIONS: 8.0-mm tracheal tubes were inserted into both the trachea and the esophagus. Tracheoscopy was performed consecutively through both tracheal tubes by a variety of clinicians. MEASUREMENTS: The times taken to correctly identify the trachea and the esophagus were recorded. MAIN RESULTS: Correct identification of either the esophagus or the trachea occurred with a 100% sensitivity and a 96% specificity. The mean time to recognize either the trachea or the esophagus was 22.0 seconds. CONCLUSIONS: Tracheoscopy is a reliable method for quickly verifying proper endotracheal placement of a tracheal tube.


Assuntos
Broncoscópios , Broncoscopia/métodos , Intubação Intratraqueal , Traqueia/anatomia & histologia , Anestesia Geral , Método Duplo-Cego , Esôfago/fisiologia , Humanos
4.
Neural Comput ; 3(2): 258-267, 1991.
Artigo em Inglês | MEDLINE | ID: mdl-31167311

RESUMO

We report on results of training backpropagation nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a stylus digitizer. Generalization results are reported as a function of training set size and network capacity. Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing hand-printed character recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy. Benefits of reducing the number of net connections, other than improving generalization, are discussed.

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