31:[] 2f:{"title":"Canadian CT Rule","cells":[{"id":0,"value":"md`# Canadian CT Rule\n\nThe Canadian computer tomography (CT) rule was developed in 2001 [1] for the assessment of minor head injury patients in the emergency department. The outcome of interest is **need for neurological intervention**. It is based on 9 boolean predictors. From a logistic regression model we may obtain the probability of the outcome.\n\n1. Stiell IG, Wells GA, Vandemheen K, et al. The Canadian CT Head Rule for patients with minor head injury. The Lancet. 2001;357(9266):1391-1396. doi:10.1016/S0140-6736(00)04561-X\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":306,"value":"chart = {\n const svg = d3.select(DOM.svg(width, height));\n \n svg.append('g')\n \t.attr('transform',`translate(${margin.left},${margin.top})`)\n .selectAll('circle')\n \t.data(layout(dataArray))\n \t.enter()\n \t.append('circle')\n \t.attr('cx', (d)=> arrayScale(d.position.x) )\n .attr('cy', (d)=> arrayScale(d.position.y) )\n .attr('r', pointRadius)\n .attr('fill',(d) => d.data );\n \n let prob = 100*predict(bf,betas(modelData));\n let prob_ci_low = 100*predict(bf,betas_ci_low(modelData));\n let prob_ci_high = 100*predict(bf,betas_ci_high(modelData));\n\n svg.append(\"g\")\n\t .append(\"text\")\n\t .attr(\"class\", \"label\")\n .style(\"font\", \"14px sans-serif\")\n\t .attr(\"x\", (width - (margin.right - margin.left)) / 2.2)\n\t .attr(\"y\", height - margin.bottom )\n\t .style(\"text-anchor\", \"middle\")\n\t .text(`Probablity (95%CI): ${prob.toFixed(1)}% (${prob_ci_low.toFixed(1)}% to ${prob_ci_high.toFixed(1)}%)`);\n \n return svg.node(); \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":570,"value":"key = {\n const svg = d3.select(DOM.svg(width, 80));\n \n svg.append('g')\n .attr('transform',`translate(${margin.left+24},${margin.top+20})`)\n .selectAll('g.key-element')\n .data(data)\n .enter()\n .append('g')\n .attr('transform',(d,i)=>`translate(0,${(i*30)})`)\n .attr('class','key-element')\n .call((parent) => {\n parent.append('circle')\n .attr('r', pointRadius)\n .attr('cx', -pointRadius*2)\n .attr('cy', -pointRadius)\t\n .attr('fill', d => d.colour)\n\n parent.append('text')\n .style(\"font\", \"14px sans-serif\")\n .attr('dx', 0)\n .attr('dy', -6) \n .text(d => d.label);\n })\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":29,"value":"viewof bf = checkbox({\n title: 'Boolean Factors',\n description: 'Please select if true for the patient',\n options: [\n \n { value: 1, label: \"Vomiting\"}, \n { value: 2, label: \"Initial GCS score 13\"}, \n { value: 3, label: \"Suspected or depressed skull fracture\"}, \n { value: 4, label: \"Any sign of basal skull fracture\"}, \n { value: 5, label: \"GCS score <15 at 2h after injury\"}, \n { value: 6, label: \"Age >= 65\"}, \n { value: 7, label: \"High-risk mechanism of injury\"},\n { value: 8, label: \"Loss of conciousness >= 5min\"}, \n { value: 9, label: \"Amnesia before impact >= 30min\"}\n ],\n value: []})","pinned":false,"hidden":false,"mode":"ojs"},{"id":553,"value":"md `### Note (from [1])\n\nCT Head Rule is only required for patients with minor head injuries\nwith any one of the following:\n\n*High risk (for neurological intervention)*\n\n● GCS score <15 at 2 h after injury\n\n● Suspected open or depressed skull fracture\n\n● Any sign of basal skull fracture (haemotympanum, ‘racoon’ eyes,\ncerebrospinal fluid otorrhoea/rhinorrhoea, Battle’s sign)\n\n● Vomiting \u0011two episodes\n\n● Age \u001165 years\n\n*Medium risk (for brain injury on CT)*\n\n● Amnesia before impact >30 min\n\n● Dangerous mechanism (pedestrian struck by motor vehicle,\noccupant ejected from motor vehicle, fall from height >3 feet or five stairs)\n\nMinor head injury is defined as witnessed loss of consciousness, definite amnesia, or witnessed disorientation in a patients with a GCS score of 13–15.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":561,"value":"md `### Note on interpretation of the icon array and probability\nThe original paper [1] did not provide a calibration curve so please treat these probabilities with some caution. Also, note the broad confidence interval.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":558,"value":"md `### Note on derivation of the rule\nThe rule is based on Canadian data 20 years old. It is possible that other jurisdictions and changes in time over how minor head injury is treated may mean the rule needs updating. Two aspects of the derivation method may improve the model too, first, not to dichotomise continuous variables, and second not to use stepwise logistic regression to choose the variables. Both these methods have been shown to be suboptimal.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":442,"value":"md`#### Model data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":142,"value":"modelData = d3.csvParse(`value,name,beta,beta_ci_low,beta_ci_high\n0,\"Intercept\",-4.27,-4.27,-4.27\n1,\"Vomiting\",1.33,0.916290732,1.740466175,\n2,\"Initial, GCS score 13\",0.79,0.18232156,1.386294361\n3,\"Suspected open or depressed skull fracture\",1.29,0.693147181,1.871802177\n4,\"Any sign of basal skull fracture\",1.65,1.223775432,2.079441542\n5,\"GCS score <15 at 2 h after injury\",1.99,1.609437912,2.370243741\n6,\"Age >=65 years\",1.42,1.029619417,1.808288771\n7,\"High-risk mechanism of injury\",1.02,0.693147181,1.335001067\n8,\"Loss of consciousness >=5 min\",0.5,0,0.955511445\n9,\"Amnesia before impact >=30 min\",0.66,0.336472237,1.029619417`)","pinned":false,"hidden":false,"mode":"ojs"},{"id":433,"value":"md`#### My functions`\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":436,"value":"function predict(f,b){\n /*. here I want to take the input from \"variables\" and turn it into some kind of vector of 1s and 0s */\n var a = 0;\n var v = [];\n for(var i=0; i