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The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb's own prior polarity - for the same verb, these two properties might even be inverse. Polarity preferences of verbs are extracted on the basis of a large and dependency- parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas the upper bound goes up to 92%.

The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb's own prior polarity - for the same verb, these two properties might even be inverse. Polarity preferences of verbs are extracted on the basis of a large and dependency- parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas the upper bound goes up to 92%.

Background and purpose: Patient outcome is often used as an indicator of quality of hospital care. The aim of this study is to investigate whether there is a straightforward relationship between quality of care and outcome, and whether outcome measures could be used to assess quality of care after stroke. Methods: In 10 centres in The Netherlands, 579 patients with acute stroke were prospectively and consecutively enrolled. Poor outcome was defined as a score on the modified Rankin scale >= 3 at 1 year. Quality of care was assessed by relating diagnostic, therapeutic and preventive procedures to indication. Multiple logistic regression models were used to compare observed proportions of patients with poor outcome with expected proportions, after adjustment for patient characteristics and quality of care parameters. Results: A total of 271 ( 47%) patients were dead or disabled at 1 year. Poor outcome varied across the centres from 29% to 78%. Large differences between centres were also observed in clinical characteristics, prognostic factors and quality of care. For example, between hospital quartiles based on outcome, age > 70 years varied from 50% to 65%, presence of vascular risk factors from 88% to 96%, intravenous fluids when indicated from 35% to 81%, and antihypertensive therapy when indicated from 60% to 85%. The largest part of variation in patient outcome between centres was explained by differences in patient characteristics ( Akaike's Information Criterion ( AIC)= 134.0). Quality of care parameters explained a small part of the variation in patient outcome ( AIC= 5.5). Conclusions: Patient outcome after stroke varies largely between centres and is, for a substantial part, explained by differences in patient characteristics at time of hospital admission. Only a small part of the hospital variation in patient outcome is related to differences in quality of care. Unadjusted proportions of poor outcome after stroke are not valid as indicators of quality of ca