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Antibiotic mechanisms and indications

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The mechanisms and uses for several groups or antibiotics are described below but resistance and preferred antibiotic choices may vary between local regions and hospital. Please refer to these when considering antibiotic choice for different infections. Antibiotic group Mechanism Indications Examples Side effects and contra-indications Penicillins (Benzylpenicillin, phenoxy-methylpenicillin)   Binds to penicillin binding protiens and inhibits the last stage of cell wall synthesis causing cell lysis. Narrow spectrum – effective against streptococcus (e.g. group A strep, strep pneumonia, s.viridans) Occaisionally effective against non penicillinase producing staphylococcus. Aminoglycosides can be added synergistically against Group B strep, s viridans and enterococcus faecalis. Respiratory infections. Syphilis. Anaphylaxis/ hypersensitivity B lactamase resistant penicillins: flucloxa
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Traumatic Brain Injury

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Traumatic brain injuries are a significant cause of morbidity and mortality. Common causes includes falls, road traffic accidents and falling objects.  Pathophysiology Traumatic brain injuries can result from a variety of mechanisms of injuries including direct impact, penetration injuries, acceleration/deacceleration injuries, or bast waves.  This results in primary injury to the brain and cranium with secondary injuries occuring later through inflammatory changes in the tissues, cell death and ischaemia from vascular injury, hypoxia, vasospasm and microocculusions.  Primary injuries may include:  Diffuse axonal injuries - caused by shearing of axons/white matter presenting with low GCS and associated with poor outcome.  Focal cerebral contusions - caused by direct impact of brain tissue on skull wall commonly seen in acceleration/deacceleration injuries. Most commonly seen in frontal and temporal areas and may be associated with intraparenchymal haematomas. 
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Significance tests

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Significance is often given as a p value where the p value is the probability of getting a result which is as extreme by chance if the null hypothesis was true (that there is no difference/correlation between 2 groups). For example if I wanted to see whether tomatoes grown in the open vs in the green house had different sizes, the null hypothesis would be that there is no difference in size and the alternative hypothesis would be that the tomatoes are either bigger or smaller. If I then I picked 5 tomatoes out of each group and measured their diameter, I cannot be sure whether any difference in average size was caused by me accidentally picking bigger tomatoes in one sample compared to the other. Also the number of tomatoes I picked to measure would affect how accurate the measurement represents the true average of all the tomatoes I have grown. The P value calculated would represent the probability of me getting the same result by chance if there was no difference in the size of the t
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