The contribution of generalist community nursing to palliative care: a retrospective case note review.

There is a paucity of evidence regarding the contribution of generalist community nursing to palliative care. The aim of this study was to identity the proportion of patients referred to a generalist community nursing service who meet the criteria for palliative care need and explore key aspects of their management. A retrospective case note review of people known to a generalist community nursing service was undertaken to identify people with palliative care needs.

Clinical predictors for laboratory-confirmed influenza infections: exploring case definitions for influenza-like illness.

Objective: To identify clinical signs and symptoms (ie, "terms") that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.

Design: Retrospective, cross-sectional study.

Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports.

Background: A highly sensitive real-time syndrome surveillance system is critical to detect, monitor, and control infectious disease outbreaks, such as influenza. Direct comparisons of diagnostic accuracy of various surveillance systems are scarce.

Objective: To statistically compare sensitivity and specificity of multiple proprietary and open source syndrome surveillance systems to detect influenza-like illness (ILI).

The impact of alternative diagnoses on the utility of influenza-like illness case definition to detect the 2009 H1N1 pandemic.

Objective: To investigate the impact of excluding cases with alternative diagnoses on the sensitivity and specificity of the Centers for Disease Control and Prevention's (CDC) influenza-like illness (ILI) case definition in detecting the 2009 H1N1 influenza, using Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification, a disease surveillance system.

Design: Retrospective cross-sectional study design.

Setting: Emergency department of an urban tertiary care academic medical center.