Ep 316 - Google Drive

Of 574 patients enrolled, 326 (56.8%) reported previous driving from 6 to 18 hours before the RTC and were eligible for analysis. The ORs (mutually adjusted) were 2.25 (95%CI 1.11-4.57) for alcohol and 0.94 (0.47-1.88) for meals. OR for alcohol was already increased at low (1-2 units) doses - 2.17 (1.03-4.57) and the trend of increase for each unit was significant - 1.64 (95%CI 1.05-2.57). In drivers at fault the OR for alcohol was 21.22 (2.31-194.79). The OR estimate for meal consumption seemed to increase in case of previous sleep deprivation, 2.06 (0.25-17.00).

Ep 316 - Google Drive

Driver-related behavioural factors are major contributors to the occurrence of road traffic crashes (RTCs), [1, 2] that in turn are the commonest cause of injury fatalities worldwide [3]. Among these factors, alcohol consumption plays an important role. In fact, 30-40% of driver deaths in the European Union result from driving under the influence of alcohol [4].

All these studies benefited from the case-crossover design, [9, 10] that has proved to be effective in estimating the risk of sudden events associated with transient exposures with short effect, such as acute alcohol consumption. However, all these studies investigated injuries of any cause - and therefore did not take into account the fact that, when RTCs are considered, the target person times at risk are driving times. In fact, it would be impossible for the drivers to be involved in collisions while not driving [10] and thus intrapersonal comparative analyses should consider only periods while the subjects are driving.

Potential study subjects were identified by ER personnel (triage nurses) who alerted trained interviewers who systematically covered selected 12-hour (weekends and nights) or 6-hour (days Monday to Friday) shifts at the ER (covered hours = 3432). The interviewers approached the eligible drivers and proposed the participation in the research, without hindering or delaying diagnostic and care activities. When possible, subjects consenting to the study were interviewed directly at the ER. The study was approved by the ethics committee of the "Azienda Ospedaliero-Universitaria" of Udine, Italy.

The questionnaire collected information on socio-demographic characteristics of the driver and driving habits, characteristics of the vehicle and the RTC, usual alcohol consumption and drink & drive habits, and other potential risk factors not reported in this article. Alcohol, food intake and driving were assessed in each of the 24 hours before the RTC. Additionally, sleep was tracked in the 48 hours before the RTC. Each interview lasted approximately 30 minutes.

During the 12 months of the study, 877 injured drivers arrived at the ER during our recruitment shifts. Of them 574 (65.5%) were enrolled; 100 subjects were injured too seriously to be interviewed, 95 refused to sign the informed consent, 40 were lost because of the contemporary arrival or more than one driver, 24 had no time to be interviewed, 22 did not understand Italian, 5 were aged

Table 4 shows the OR for alcohol and meals from the full model stratified by potential effect modifiers. Alcohol use in the previous 6 hours and being at fault strongly interacted in triggering the crash. A lower level of education also increased the risk - OR for higher education 2.16 (95% CI 0.91-5.14) and OR for lower education 5.99 (95% CI 1.04-34.57). Some other subgroups of subjects exposed to alcohol (drivers who held a license for less than 5 years, females, the younger than 25 years and those with lower habitual consumption) show a possible, non significant, association with a greater risk of RTC.

Although based on small numbers, certain subgroups of drivers showed stronger effects of alcohol on RTCs (table 4). The interpretation of these findings is difficult, also because some of these variables could be proxy of each other. A possible explanation could be that the effects are larger when drinking occurs in the unaccustomed and/or in a sporadic, binge-like fashion. This would be consistent with a previous report [5] but not with another one [8]. Moreover, the results about usual consumption should be taken with caution because it has been shown [18] that the simple estimation of usual consumption may mask a wide range of drinking patterns (i.e., low and steady or infrequent and heavy) that carry different risks. Finally, because of both the definition of alcohol exposure in our study (i.e. any quantity in the 6 hours) and the likely existence of a dose-effect, the modification of risk could be spurious, i.e. merely reflect a higher intake on average in the exposure windows. The sharp increase in effect with increasing culpability adds to the reliability of our methods.

The case-crossover design offers the conspicuous advantage of eliminating interpersonal confounding and problems in the selection of control groups. Its previous applications to the study of risk factors for RTCs have been successful [13, 14, 21]. However, this particular application requires the fulfilment of the 'driving opportunity' criterion, i.e. a special definition of time at risk that should be 'time while driving'. Since previous studies targeted all injuries, they did not restrict the hazard period to time while driving. Moreover, in these studies not only drivers but all injured patients were included. It is questionable though if alcohol assumption by a transported passenger can have any role in the causation of a RTC as indirectly shown also by our data on effect modification by culpability.

Secondly, it has to be recognized, that the 'driving opportunity' criterion is truly fulfilled when the subject drives for the same fraction of time within the case and the control window. Similarly, other conditions that may have affected the risk of RTC while driving (e.g. weather and road conditions, traffic congestion etc.) had to be arbitrarily assumed as similar in the 2 windows. The alternative would have been to record this information in both windows and account for them in the analysis but this would have been very impractical and difficult.

Simultaneously meeting all the prescribed parameter ranges (Fig. 2, grey bars) represents a breakthrough in every aspect: the demonstration of laser-driven proton-beam generation at this high-energy level over two years, dose delivery at radiobiologically relevant quality and high-precision dosimetry over many weeks and for every shot of each required day.

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I haven't gone into details with the datasheet, but I think this driver is specially designed to drive IGBT's. I think you'd better pick a different IC when driving mosfets.Regarding simulation: I got better simulation results by shorting/disabling the Integrated Desaturation (VCE) Detection to ground.BTW, You don't need V4, because it is only needed when negative gate drive is implemented.

WORKING SIMULATIONNote I did not use MUR1100. It takes ages on my PC to simulate with it. Moreover, I questioned before why you want to use this DESAT pin or actually even this IGBT driver. I got almost the same results by deleting this D1 and connecting the 100 ohm resistor to GND.For clarity, I also removed almost all caps that are connected directly across a voltage source (except for C2) because in simulation these caps don't do anything. (But do add them on the real PCB!)You should look at the diagram on page 3: that reveals why applying a negative voltage on VINN is useless; better connect it to GND. The same for the pulse source / positive triangular pulse source connected to VINP.I connected a 1 Meg to LED1 in order to see the output of the internal comparator. For your PCB, the datasheet suggest to leave LED1 and LED2 unconnected.

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