Database

IARC on Glyphosate – what to do when a mistake is made? The Governing Council of the International Agency for Research on Cancer (IARC[1]) is meeting[2] today and tomorrow. Not listed on the published agenda is glyphosate, but much of the conversation will be about the hotly disputed decision[3] that glyphosate is ‘probably carcinogenic to humans’ (Group 2A). Was it the right finding, why was so much of the animal experimentation evidence deemed unsuitable for consideration, is it ethical to make pronouncements of any sort if there is no published evidence of how often IARC decisions are wrong? How should scientific expert opinion be held to account? Who underwrites the effect of mistakes? Is it ethical not to take responsibility for mistakes? Holding IARC to account It seems obvious in hindsight that institutions of all kinds whether commercial or public should publish an account of how accurate their published findings are[4]. In time, false positives, false negatives, true positives

Just occasionally someone asks if everything we believe about causation-related science is wrong. This time, the use of the t-test is a cause of doubt. In the interpretation of rat lab results, animal experimenters use the t-test. The t-test, when used as originally designed, compares the means of two data distributions. The standard deviation of the distribution is first reduced to the standard error of the mean (SEM) and compared with the mean and SEM of the other distribution. If p < 0.05 it is pronounced that the two distributions are probably different. So, when comparing control animals with those dosed with a toxin the t-test is used to detect the likelihood that the toxin did anything. The reason for doubt is that SEM comparisons are only valid for true means. The single result e.g. 4 out of 90 rats developed lung cancer, is not a mean. Despite this, experimental scientists use the t-test to decide if 4/90 is different from 5/90. In fact the same experiment, when repeated, h

glyphosate meta-analysis and non-Hodgkin lymphoma glyphosate meta-analysis Does Glyphosate cause non-Hodgkin lymphoma (NHL)? Observing that different studies in herbicide application workers give very different results, the authors of a recent[1] meta-analysis have proposed a new approach. By choosing those results which correspond with the highest exposure in each study, and ignoring all the rest, it might be possible to detect a causal association, if there is one. It being self-evident that the meta-risk ratio by itself would have no meaning, unless the high exposures were very similar, the main task is to show that the meta-risk ratio is statistically significant. Significance would suggest that a causal association was possible, even if no-one could tell how strong the association was. Significance testing in the biological sciences centres on whether or not the 95% confidence interval (95% CI) includes 1.0. This is a convention, not universally agreed with. In the recent paper th

Tech Plus reminder robot toxicologist Data mining and machine learning look set to revolutionise knowledge management in routine business processes[1]. Can they be used to assist with the identification and evaluation of new liability risks? The Robotic Toxicologist report presents an expert-based analysis of a liability-related work recently published by Allianz[2] et al. The Allianz et al. report demonstrated some very appealing underlying capabilities. However, it is clear there are areas where targeted expert assistance could lead to improvements in toxicological insight, relevance and liability meaning-making. Insurers wishing to develop or acquire robotic tools could include such targeted expertise in their development and evaluation projects and if they are adopted, in the management of machine outputs. The Robotic Toxicologist Report. Could an automated search machine usefully identify injury outcomes associated with three of the chemicals in nail varnish? There were 15 overt “

In these days of machine learning solutions for business optimisation, one key question is whether machines can usefully pick out emerging liability risks. The Robot Toxicologist The Robot Toxicologist The “Toxic Trio” as a case study Wouldn’t it be wonderful if a machine could read all of the world’s science literature, decide which substance would trigger new liability exposures, say how much this would cost and who should pay? After > 10 years of development work, the recent marketing document[1] from Allianz illustrates how far along this path one particular robot has travelled. UK liability insurers read the Allianz report and asked –‘is it better than tossing a coin’? 51% is seen as the minimum requirement for authorising reserves for example. The task was to compare the fifteen substantial findings in the report (in the context of nail varnish) with the written views of expert toxicology committees produced over several decades. Is this a fair test? One of the key features of

Vibration White Finger A modelling case study An epidemiology-based approach to liability ENID modelling has been developed and applied[1]. While based on the same concepts, in practice each scenario-specific ENID model is mathematically unique. This note describes the approach using the example of vibration white finger (VWF[2]). The results agree, within tolerance, with official data.   Brief background Long term exposure to high intensity vibrations leads to a predisposition to episodes of finger blanching. In severe cases there is loss of dexterity. Cause and severity of VWF are both cumulative in nature. A typical presentation is illustrated below: The cause of these symptoms is an autonomic[3] constriction of the blood vessels supplying parts of the hands[4]. Episodes of finger blanching may be provoked by vibration, cold weather and wetting with water. A similar effect is seen in Raynaud’s’ phenomenon (RP) which is of constitutional origin. This initially gave rise to uncer