Neutrophilia is associated with a poorer clinical outcome in dogs with chronic hepatitis.

BACKGROUND: Liver disease is a common cause of morbidity and mortality in dogs. Currently, it is challenging to prognosticate in these cases. The aim of this study was to evaluate the utility of the haematological variables in dogs with chronic hepatitis. METHODS: Dogs with chronic hepatitis confirmed on histopathology had presenting haematological values retrospectively obtained and evaluated against survival time. Eighty-two dogs met the inclusion criteria and their data analysed. RESULTS: Neutrophilic patients, with a count greater than 12×109/l, controlled for sex and age, had a shorter survival time (P≤0.01). In dogs, neutrophilia at presentation predicted a poor outcome, whereas the other haematological parameters were not prognostically informative. When the dogs were split into even quarters on the basis of their neutrophil count, those within the higher quartiles had poorer survival times. Neutrophilia was associated with a poorer survival time in comparison to those patients with a lower count. CONCLUSION: The relationship between neutrophils, inflammation and clinical outcome is deserving of future study in dogs with chronic hepatitis.

Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence.

We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to "censoring" or exclusion of potentially relevant data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.