HIV management by nurse prescribers compared with doctors at a paediatric centre in Gaborone, Botswana

Objectives. To compare compliance with national paediatric HIV treatment guidelines between nurse prescribers and doctors at a paediatric referral centre in Gaborone, Botswana. Methods. A cross-sectional study was conducted in 2009 at the Botswana-Baylor Children’s Clinical Centre of Excellence (COE), Gaborone, Botswana, comparing the performance of nurse prescribers and physicians caring for HIV-infected paediatric patients. Selected by stratified random sampling, 100 physician and 97 nurse prescriber encounters were retrospectively reviewed for successful documentation of eight separate clinically relevant variables: pill count charted; chief complaint listed; social history updated; disclosure reviewed; physical exam; laboratory testing; World Health Organization (WHO) staging documented; paediatric dosing. Results. Nurse prescribers and physicians correctly documented 96.0% and 94.9% of the time, respectively. There was a trend towards a higher proportion of social history documentation by the nurses, but no significant difference in any other documentation items. Conclusions. Our findings support the continued investment in programmes employing properly trained nurses in southern Africa to provide quality care and ART services to HIV-infected children who are stable on therapy. Task shifting remains a promising strategy to scale up and sustain adult and paediatric ART more effectively, particularly where provider shortages threaten ART rollout. Policies guiding ART services in southern Africa should avoid restricting the delivery of crucial services to doctors, especially where their numbers are limited

CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking

How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by the solutions. In this paper, we explicitly consider long-term tracking scenarios and provide a framework, named CoCoLoT, that combines the characteristics of complementary visual trackers to achieve enhanced long-term tracking performance. CoCoLoT perceives whether the trackers are following the target object through an online learned deep verification model, and accordingly activates a decision policy which selects the best performing tracker as well as it corrects the performance of the failing one. The proposed methodology is evaluated extensively and the comparison with several other solutions reveals that it competes favourably with the state-of-the-art on the most popular long-term visual tracking benchmarks