Tissue plasminogen activator for acute ischemic stroke. The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group.

Interobserver agreement for the assessment of handicap in stroke patients was investigated in a group of 10 senior neurologists and 24 residents from two centers. One hundred patients were separately interviewed by two physicians in different combinations. The degree of handicap was recorded by each observer on the modified Rankin scale, which has six grades (0-5). The agreement rates were corrected for chance (kappa statistics). Both physicians agreed on the degree of handicap in 65 patients; they differed by one grade in 32 patients and by two grades in 3 patients. Kappa for all pairwise observations was 0.56; the value for weighted kappa (with quadratic disagreement weights) was 0.91. Our results confirm the value of the modified Rankin scale in the assessment of handicap in stroke patients; nevertheless, further improvements are possible.

Interobserver agreement for the assessment of handicap in stroke patients.

Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration

What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

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Causation, prediction, and search

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book.

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Bayesian networks and decision graphs

Predicting outcome in individual patients after severe head-injury

Background The eradication of Helicobacter pylori infection is beneficial in patients with gastric or duodenal ulcers. The value of eradicating the infection in patients with dyspepsia and no evidence of ulcer disease is not known. Methods We performed a randomized, placebo-controlled trial comparing the efficacy of treatment for two weeks with 20 mg of omeprazole orally twice daily, 500 mg of amoxicillin three times daily (with 500 mg of tetracycline three times daily substituted for amoxicillin in patients allergic to penicillin), and 400 mg of metronidazole three times daily (160 patients) with that of omeprazole alone (158 patients) for resolving symptoms of dyspepsia in patients with H. pylori infection but no evidence of ulcer disease on upper gastrointestinal endoscopy. Symptoms were assessed with the Glasgow Dyspepsia Severity Score, with resolution of symptoms defined as a score of 0 or 1 in the preceding six months (maximal score, 20). One year later the patients were assessed to determine the f...

https://www.nejm.org/doi/pdf/10.1056/NEJM199812243392601

Symptomatic Benefit from Eradicating Helicobacter pylori Infection in Patients with Nonulcer Dyspepsia

Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology

We conducted serial neurologic examinations on 500 patients in nontraumatic coma to identify factors predicting recovery. Overall, 81 patients (16%) led an independent life at some point within the first year; the remainder either died without recovery from coma (61%), never improved beyond the vegetative state (12%), or regained consciousness but remained dependent on others for daily activities (11%). Functional recovery did not depend on age but was to some degree related to the cause of coma (subarachnoid hemorrhage and other cerebrovascular disease having the worst recovery; hypoxia-ischemia, intermediate; and hepatic and miscellaneous causes, best) and especially to early clinical signs of brain dysfunction. Even within hours of the onset of coma, only one of 120 patients lacking two of corneal, pupillary, and oculovestibular responses ever regained independent function. The study identifies clinical features of comatose patients that appear within the first week and that are important for predicting recovery and designing future therapeutic trials.

Robin P. Knill-Jones

Papers

Predicting outcome in individual patients after severe head-injury

Symptomatic Benefit from Eradicating Helicobacter pylori Infection in Patients with Nonulcer Dyspepsia

Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology

Prognosis in Nontraumatic Coma

ANÆSTHETIC PRACTICE AND PREGNANCY: Controlled Survey of Women Anæsthetists in the United Kingdom